# agent-as-model (pypi · agent-as-model)

A true MCP server where the agent is the model and the API key. No API keys.

- Trust score: 84/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- pypi · `agent-as-model`: 84/100 (this document), [markdown](https://verifymcp.io/servers/fernandogarzaaa-agent-as-model/agent-as-model.md), [page](https://verifymcp.io/servers/fernandogarzaaa-agent-as-model/agent-as-model)

## Channel facts

- Registry: `pypi`
- Package: `agent-as-model`
- Version: `0.2.0`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-05.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - Runs setuptools.build_meta at install time, a recognised build step with no custom scripting around it.
  - 1 of 28 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 97/100
  - Source repository is publicly reachable at the declared URL.
  - Cryptographically verified build provenance (signed, bound to fernandogarzaaa/agent-as-model).
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 16 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 48/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 3681 tokens (~736/item across 5 items; 5 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 57/100
  - Stability observed for 17 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (80% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### How do I install the agent-as-model MCP server?

agent-as-model runs locally as a PyPI package, launched with uvx agent-as-model. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add fernandogarzaaa-agent-as-model -- uvx agent-as-model
```

### Cursor

```json
{
  "mcpServers": {
    "fernandogarzaaa-agent-as-model": {
      "command": "uvx",
      "args": [
        "agent-as-model"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "fernandogarzaaa-agent-as-model": {
      "command": "uvx",
      "args": [
        "agent-as-model"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add fernandogarzaaa-agent-as-model -- uvx agent-as-model
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "fernandogarzaaa-agent-as-model": {
      "type": "local",
      "command": [
        "uvx",
        "agent-as-model"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add fernandogarzaaa-agent-as-model --command uvx --arg agent-as-model
```

### Hermes

```yaml
mcp_servers:
  fernandogarzaaa-agent-as-model:
    command: "uvx"
    args: ["agent-as-model"]
```

### Netclaw

```json
{
  "McpServers": {
    "fernandogarzaaa-agent-as-model": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "agent-as-model"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add fernandogarzaaa-agent-as-model -t stdio -c uvx -a agent-as-model
```

### Other

```json
{
  "mcpServers": {
    "fernandogarzaaa-agent-as-model": {
      "command": "uvx",
      "args": [
        "agent-as-model"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-05 (score 84, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-02 (score 83, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-31 (score 82, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-29 (score 81, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-27 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-25 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-23 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-21 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (5)

### `chat_complete` (~937 tokens)

Generate a chat completion from an OpenAI chat-completions-shaped request. The completion is produced by the host agent's own model: direct MCP sampling on pre-2026-07-28 connections, an MRTR InputRequiredResult carrying the request payload on 2026-07-28+. When output_schema is given, the request carries exactly one required tool whose input schema is that schema, and the validated structured completion is returned as JSON text. When tools is given, the tool definitions are forwarded to the host's model with tool_choice, but only if the host advertised the sampling.tools capability: otherwise the tools are dropped (a warning is logged server-side) and the call degrades to a plain-text completion. When the host answers by calling tools, the returned text is JSON: {"text": "...", "tool_calls": [{"id": "...", "input": {...}, "name": "..."}]} (keys sorted). A plain-text host answer returns text as before. tools and output_schema cannot be combined. An optional persona {name, input_schema, output_schema} registers a per-call contract: the request arguments are validated against input_schema before anything is sent, and the host's completion text must parse as JSON validating against output_schema. The server is stateless and keeps no persona registry. persona.output_schema and output_schema cannot be combined. Session cursors: the server keeps no session state. To continue a conversation, pass the accumulated history back in and the previous turn's cursor as previous_response_id: '' starts a cursor chain (nothing verified), 'cur_<64 hex>' continues a verified chain (checked against the history passed back; mismatch fails loud naming expected vs received), 'resp_<32 hex>' (a responses_create response id) anchors a new chain at the passed history. When previous_response_id is given, the completion returns as JSON '{"completion": "<text>", "response_id": "cur_<...>"}' instead of plain text; when omitted the tool returns plain text exactly as before. The cursor is 'cur_' +…

Input parameters:

- `max_tokens` (integer): Maximum tokens to generate.
- `messages` (array, required): Chat messages; system roles become the system prompt.
- `model` (string): Model alias; agent-default means the host's own model.
- `output_schema`: Optional JSON Schema (root type object). When given, the sampling request carries exactly one required tool with this input schema and the completion is validated against it before returning.
- `persona`: Optional persona contract for this call: {name, input_schema, output_schema}. name is required and non-empty; the server is stateless and keeps no persona registry. When input_schema is given, the re…
- `previous_response_id`: Optional session cursor for host-owned multi-turn continuations. The server keeps no session state: pass the accumulated history back in messages and the previous turn's cursor here. '' starts a curs…
- `temperature`: Sampling temperature, if the host honors it.
- `tool_choice`: Optional tool-choice mode, forwarded with tools; defaults to 'auto' when tools are given.
- `tools`: Optional tool definitions to forward to the host's model. Each is {name, description, input_schema} where input_schema is a JSON Schema object with root type 'object'. Forwarded only when the host ad…

Output parameters:

- `result` (string)

### `messages_create` (~944 tokens)

Generate a completion from an Anthropic messages-shaped request. The completion is produced by the host agent's own model: direct MCP sampling on pre-2026-07-28 connections, an MRTR InputRequiredResult carrying the request payload on 2026-07-28+. When output_schema is given, the request carries exactly one required tool whose input schema is that schema, and the validated structured completion is returned as JSON text. When tools is given, the tool definitions are forwarded to the host's model with tool_choice, but only if the host advertised the sampling.tools capability: otherwise the tools are dropped (a warning is logged server-side) and the call degrades to a plain-text completion. When the host answers by calling tools, the returned text is JSON: {"text": "...", "tool_calls": [{"id": "...", "input": {...}, "name": "..."}]} (keys sorted). A plain-text host answer returns text as before. tools and output_schema cannot be combined. An optional persona {name, input_schema, output_schema} registers a per-call contract: the request arguments are validated against input_schema before anything is sent, and the host's completion text must parse as JSON validating against output_schema. The server is stateless and keeps no persona registry. persona.output_schema and output_schema cannot be combined. Session cursors: the server keeps no session state. To continue a conversation, pass the accumulated history back in and the previous turn's cursor as previous_response_id: '' starts a cursor chain (nothing verified), 'cur_<64 hex>' continues a verified chain (checked against the history passed back; mismatch fails loud naming expected vs received), 'resp_<32 hex>' (a responses_create response id) anchors a new chain at the passed history. When previous_response_id is given, the completion returns as JSON '{"completion": "<text>", "response_id": "cur_<...>"}' instead of plain text; when omitted the tool returns plain text exactly as before. The cursor is 'cur_' + sha256 ove…

Input parameters:

- `max_tokens` (integer): Maximum tokens to generate.
- `messages` (array, required): User/assistant messages in order.
- `model` (string): Model alias; agent-default means the host's own model.
- `output_schema`: Optional JSON Schema (root type object). When given, the sampling request carries exactly one required tool with this input schema and the completion is validated against it before returning.
- `persona`: Optional persona contract for this call: {name, input_schema, output_schema}. name is required and non-empty; the server is stateless and keeps no persona registry. When input_schema is given, the re…
- `previous_response_id`: Optional session cursor for host-owned multi-turn continuations. The server keeps no session state: pass the accumulated history back in messages and the previous turn's cursor here. '' starts a curs…
- `system`: System prompt as text or text blocks.
- `temperature`: Sampling temperature, if the host honors it.
- `tool_choice`: Optional tool-choice mode, forwarded with tools; defaults to 'auto' when tools are given.
- `tools`: Optional tool definitions to forward to the host's model. Each is {name, description, input_schema} where input_schema is a JSON Schema object with root type 'object'. Forwarded only when the host ad…

Output parameters:

- `result` (string)

### `responses_create` (~1199 tokens)

Generate a completion from an OpenAI Responses-shaped request, returning the Responses API response object. The completion is produced by the host agent's own model: direct MCP sampling on pre-2026-07-28 connections, an MRTR InputRequiredResult carrying the request payload on 2026-07-28+. `input` is a string or a list of input items; each item is {role, content} with role user, assistant, system, or developer, and content a string or a list of text parts ({type: 'input_text'|'text', input_text: '...'}). A string input becomes one user message. System and developer items fold into the system prompt alongside `instructions`, which leads. Non-text parts are rejected. `max_tokens` is named for uniformity with the other tools (the Responses API calls it max_output_tokens); same meaning. When output_schema is given, the request carries exactly one required tool whose input schema is that schema, and the validated structured completion is returned as JSON text inside the output_text. When tools is given, the tool definitions are forwarded to the host's model with tool_choice, but only if the host advertised the sampling.tools capability: otherwise the tools are dropped (a warning is logged server-side) and the call degrades to a plain-text completion. When the host answers by calling tools, the output array carries the accompanying text as a message item (omitted when empty) followed by one function_call item per call: {type: 'function_call', id: 'fc_<call id>', call_id: '<host tool-call id>', name: '...', arguments: '<JSON>'}; the host's tool-call id maps to call_id, the object id is derived from it. A plain-text host answer returns a single message item. tools and output_schema cannot be combined. An optional persona {name, input_schema, output_schema} registers a per-call contract: the request arguments are validated against input_schema before anything is sent, and the host's completion text must parse as JSON validating against output_schema. The server is stateless…

Input parameters:

- `input` (required): The Responses API input: a string, which becomes one user message, or a list of input items {role, content}. role is user, assistant, system, or developer; content is a string or a list of text parts…
- `instructions`: Optional system prompt; leads the system/developer items.
- `max_tokens` (integer): Maximum tokens to generate. Named max_tokens for uniformity with the other tools; the Responses API calls it max_output_tokens.
- `model` (string): Model alias; agent-default means the host's own model.
- `output_schema`: Optional JSON Schema (root type object). When given, the sampling request carries exactly one required tool with this input schema and the completion is validated against it before returning.
- `persona`: Optional persona contract for this call: {name, input_schema, output_schema}. name is required and non-empty; the server is stateless and keeps no persona registry. When input_schema is given, the re…
- `previous_response_id`: Optional session cursor for host-owned multi-turn continuations. The server keeps no session state: pass the accumulated history back in input and the previous turn's response id here. '' starts a cu…
- `temperature`: Sampling temperature, if the host honors it.
- `tool_choice`: Optional tool-choice mode, forwarded with tools; defaults to 'auto' when tools are given.
- `tools`: Optional tool definitions to forward to the host's model. Each is {name, description, input_schema} where input_schema is a JSON Schema object with root type 'object'. Forwarded only when the host ad…

### `generate` (~539 tokens)

Generate a raw text completion from a plain prompt string. The prompt is sent as a single user sampling message with no system prompt and no chat formatting: raw prompt in, completion text out, produced by the host agent's own model. Same model hint and parameters as the other completion tools, minus any chat or tool features: no tools, no output_schema, no tool_choice. It does accept the optional persona contract ({name, input_schema, output_schema}): request/response validation only, never a sampling feature, so the thin path stays zero overhead. Session cursors: the server keeps no session state. previous_response_id '' mints a cursor over the current prompt and completion, returned as JSON '{"completion": "<text>", "response_id": "cur_<...>"}' instead of plain text; when omitted the tool returns plain text exactly as before. generate has no structured history channel (the prompt is the only input), so a 'cur_' cursor can never be verified here: passing one fails loud instead of silently skipping the check. The cursor is 'cur_' + sha256 over canonical JSON '{"model","tool","transcript"}' (sorted keys, compact separators) with the transcript [{"role": "user", "content": <prompt>}, {"role": "assistant", "content": <completion>}], so the caller can recompute it. No API key is used or needed: the connected agent is the model.

Input parameters:

- `max_tokens` (integer): Maximum tokens to generate.
- `model` (string): Model alias; agent-default means the host's own model.
- `persona`: Optional persona contract for this call: {name, input_schema, output_schema}. name is required and non-empty; the server is stateless and keeps no persona registry. When input_schema is given, the re…
- `previous_response_id`: Optional session cursor for host-owned continuations. '' mints a cursor over the current prompt and completion, returned as JSON '{"completion": "<text>", "response_id": "cur_<...>"}' instead of plai…
- `prompt` (string, required): The raw prompt text to complete. No chat formatting applied.
- `temperature`: Sampling temperature, if the host honors it.

Output parameters:

- `result` (string)

### `list_models` (~33 tokens)

List the models this server offers. There is exactly one: agent-default, an alias for the host agent's own model.

Output parameters:

- `result` (string)

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/fernandogarzaaa-agent-as-model/agent-as-model#diagnostics

## Score history

- 2026-11-05: 84
- 2026-11-04: 83
- 2026-11-03: 83
- 2026-11-02: 83
- 2026-11-01: 82
- 2026-10-31: 82
- 2026-10-30: 81
- 2026-10-29: 81
- 2026-10-28: 80
- 2026-10-27: 80
- 2026-10-26: 79
- 2026-10-25: 79
- 2026-10-24: 78
- 2026-10-23: 78
- 2026-10-22: 77
- 2026-10-21: 77
- 2026-10-20: 76
- 2026-10-19: 76

## Common questions

### What is the agent-as-model MCP server?

agent-as-model is an MCP server listed in the public MCP registry as io.github.fernandogarzaaa/agent-as-model. A true MCP server where the agent is the model and the API key. No API keys. This page covers its PyPI package (agent-as-model).

### Is the agent-as-model MCP server safe to use?

agent-as-model scores 84 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the agent-as-model MCP server expose?

agent-as-model exposes 5 tools: chat_complete, messages_create, responses_create, generate, list_models. Their descriptions and schemas cost roughly 3,652 tokens of context every time the server is loaded.

### Is the agent-as-model MCP server still maintained?

agent-as-model is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

### What licence is the agent-as-model MCP server under?

agent-as-model declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- PyPI project: https://pypi.org/project/agent-as-model/
- Socket report: https://socket.dev/pypi/package/agent-as-model
- Repository: https://github.com/fernandogarzaaa/agent-as-model
- Changelog RSS feed: https://verifymcp.io/servers/fernandogarzaaa-agent-as-model/agent-as-model.xml
- Changelog JSON feed: https://verifymcp.io/servers/fernandogarzaaa-agent-as-model/agent-as-model.json
- HTML version of this page: https://verifymcp.io/servers/fernandogarzaaa-agent-as-model/agent-as-model