io.github.karmendra8386/agent-cost-mcp-server
PYPI · AGENT-COST-MCP-SERVER
Prices LLM/agent runs per successful task, with spend recording and budget status.
Available components
Last scanned (21 hours ago)
Scores higher than 76% of PyPI servers
Category AI and Machine Learning
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. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 0 of 28 dependencies flagged as unhealthy. View diagnostics → Pass
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to karmendra8386/agent-cost-mcp-server). View diagnostics → Pass
- Clear OSI-approved license (Apache-2.0).Pass
- Actively maintained (last published 1 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability73
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 585 tokens (~117/item across 5 items; 5 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
- Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage67
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Unverified: 1 category
A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Nov 26 78
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 4 Nov 2026 · Analysed pypi/agent-cost-mcp-server@0.1.1
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | karmendra8386/agent-cost-mcp-server |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/karmendra8386/agent-cost-mcp-server/.github/workflows/publish.yml@refs/heads/master |
| Rekor log index | 3656135482 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:5d23b135161e43df9a66684e87e2507057d90476f9e6beb2702d6fe21bc25f0b |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
budget_status ~102
Check a task's recorded spend against a budget. Args: task_id: The task identifier used with record_spend. budget_usd: The budget for the task, in USD. Returns spent, remaining, call count, and whether the task is over budget. This server supplies the numbers; enforcing the budget (stopping a run) belongs to the agent framework.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_usd | number | yes | – |
| task_id | string | yes | – |
No output schema declared.
No examples provided.
compare_models_for_task ~153
Rank models for a task by expected cost per successful task. Cost per token is the wrong ranking for agent work: a cheap model that fails half its tasks costs more per success than a pricier, reliable one. Pass measured success rates (0-1 per model_id) if you have them; models without a supplied rate are ranked on raw cost. Args: input_tokens: Input tokens for one attempt at the task. output_tokens: Output tokens for one attempt at the task. success_rates: Optional map of model_id to success rate in (0, 1].
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | integer | yes | – |
| output_tokens | integer | yes | – |
| success_rates | – | – | – |
No output schema declared.
No examples provided.
estimate_run_cost ~103
Estimate what one run of a workload will cost on a model. Args: model_id: Model to price the workload on. input_tokens: Expected input tokens for one run. output_tokens: Expected output tokens for one run. Returns the model's cost plus the same workload priced on every other model in the snapshot, cheapest first.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | integer | yes | – |
| model_id | string | yes | – |
| output_tokens | integer | yes | – |
No output schema declared.
No examples provided.
price_usage ~116
Price a concrete usage record at a model's list rates. Args: model_id: Model identifier from the pricing snapshot, e.g. "anthropic.claude-sonnet-4-5" or "openai.gpt-5-mini". input_tokens: Input (prompt) tokens consumed. output_tokens: Output (completion) tokens consumed. Returns the input/output/total cost in USD.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | integer | yes | – |
| model_id | string | yes | – |
| output_tokens | integer | yes | – |
No output schema declared.
No examples provided.
record_spend ~111
Record a run's actual token usage in the local per-task ledger. Args: task_id: Caller-chosen identifier grouping one task's runs. model_id: Model the run used. input_tokens: Actual input tokens consumed. output_tokens: Actual output tokens consumed. Returns the recorded entry, priced at the model's list rates.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | integer | yes | – |
| model_id | string | yes | – |
| output_tokens | integer | yes | – |
| task_id | string | yes | – |
No output schema declared.
No examples provided.
How do I install the io.github.karmendra8386/agent-cost-mcp-server server?
io.github.karmendra8386/agent-cost-mcp-server runs locally as a PyPI package, launched with uvx agent-cost-mcp-server. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · agent-cost-mcp-server
claude mcp add karmendra8386-agent-cost-mcp-server -- uvx agent-cost-mcp-server
What is the io.github.karmendra8386/agent-cost-mcp-server server?
io.github.karmendra8386/agent-cost-mcp-server is listed in the public MCP registry as io.github.karmendra8386/agent-cost-mcp-server. Prices LLM/agent runs per successful task, with spend recording and budget status. This page covers its PyPI package (agent-cost-mcp-server).
Is the io.github.karmendra8386/agent-cost-mcp-server server safe to use?
io.github.karmendra8386/agent-cost-mcp-server scores 78 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 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 io.github.karmendra8386/agent-cost-mcp-server server expose?
io.github.karmendra8386/agent-cost-mcp-server exposes 5 tools: price_usage, estimate_run_cost, compare_models_for_task, record_spend, budget_status. Their descriptions and schemas cost roughly 585 tokens of context every time the server is loaded.
Is the io.github.karmendra8386/agent-cost-mcp-server server still maintained?
io.github.karmendra8386/agent-cost-mcp-server is still listed as active in the MCP registry. We last reached this channel on 4 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 io.github.karmendra8386/agent-cost-mcp-server server under?
io.github.karmendra8386/agent-cost-mcp-server declares the Apache-2.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.