io.github.MukundaKatta/agentfit
NPM · @MUKUNDAKATTA/AGENTFIT-MCP · SCANNED SEP 22
Token-aware message truncation: fit a chat history into your model's context budget.
Available components
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 Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 31 of 96 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 147 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability80
- AI-judged instruction clarity (excellent).Pass
- Tool/resource definitions use about 359 tokens (~119/item across 3 items; 3 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management97
- Stability observed for 29 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "fit_messages" implies "drop" and declares no destructiveHint at all, which the MCP spec reads as destructive by default. See how to fix → Fail
- An AI judge read all 3 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
How do I install the io.github.MukundaKatta/agentfit MCP server?
io.github.MukundaKatta/agentfit runs locally as an npm package, launched with npx -y @mukundakatta/agentfit-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · @mukundakatta/agentfit-mcp
claude mcp add mukundakatta-agentfit -- npx -y @mukundakatta/agentfit-mcp
{
"mcpServers": {
"mukundakatta-agentfit": {
"command": "npx",
"args": [
"-y",
"@mukundakatta/agentfit-mcp"
]
}
}
} {
"servers": {
"mukundakatta-agentfit": {
"command": "npx",
"args": [
"-y",
"@mukundakatta/agentfit-mcp"
]
}
}
} codex mcp add mukundakatta-agentfit -- npx -y @mukundakatta/agentfit-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"mukundakatta-agentfit": {
"type": "local",
"command": [
"npx",
"-y",
"@mukundakatta/agentfit-mcp"
],
"enabled": true
}
}
} openclaw mcp add mukundakatta-agentfit --command npx --arg -y --arg @mukundakatta/agentfit-mcp
mcp_servers:
mukundakatta-agentfit:
command: "npx"
args: ["-y", "@mukundakatta/agentfit-mcp"] {
"McpServers": {
"mukundakatta-agentfit": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"@mukundakatta/agentfit-mcp"
]
}
}
} assistant mcp add mukundakatta-agentfit -t stdio -c npx -a -y @mukundakatta/agentfit-mcp
{
"mcpServers": {
"mukundakatta-agentfit": {
"command": "npx",
"args": [
"-y",
"@mukundakatta/agentfit-mcp"
]
}
}
} 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.
- 22 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 21 Sept 26 0
- Security disclosure: unverified → fail ▼ functional
- 20 Sept 26 +1
- Security disclosure: fail → unverified ▼ functional
- 17 Sept 26 −2
- Stability: pass → 0.80 functional
- 16 Sept 26 0
- Stability: 0.97 → pass security
- 15 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 13 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 87 to 90. That category is still filling its 30-day observation window: 26 days of observed history at the previous scan, 27 at this one. The score rises as the window fills, whether or not the server changes.
- 10 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 77 to 80. That category is still filling its 30-day observation window: 23 days of observed history at the previous scan, 24 at this one. The score rises as the window fills, whether or not the server changes.
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 22 Sept 2026 · Analysed npm/@mukundakatta/agentfit-mcp@0.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 96 packages
| Packages resolved | 96 |
|---|---|
| Stale | 31 |
| 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 →
count_tokens ~131
Estimate tokens in a string or chat-message array. Fast, dependency-free, within ~10-20% of true tokenizer counts on English prose. Pass a model name to pick the right per-family estimator (openai, anthropic, google, llama, default).
| Name | Type | Req | Description |
|---|---|---|---|
| input | – | yes | String or array of chat messages. |
| model | string | – | Optional model name (e.g. "gpt-5", "claude-sonnet-4-6"). Picks the closest estimator family. |
| overhead | number | – | Per-message overhead in tokens (default depends on model family, usually 4-6). |
No output schema declared.
No examples provided.
fit_messages ~194
Drop messages from the input array until the total is under maxTokens. Three strategies: drop-oldest (default), drop-middle, priority (uses each message's `priority` field). Always returns a structured result with token counts before and after; never throws across the wire.
| Name | Type | Req | Description |
|---|---|---|---|
| maxTokens | number | yes | Token budget the result must come in under. |
| messages | array | yes | Chat messages to fit. |
| model | string | – | Optional model name for estimator selection. |
| overhead | number | – | Per-message overhead in tokens. |
| preserveFirstN | number | – | Never drop the first N messages of the input array. Default 0. |
| preserveLastN | number | – | Never drop the last N messages of the input array. Default 0. |
| preserveSystem | boolean | – | Default true: never drop messages with role === "system". |
| strategy | string | – | Drop strategy. Default drop-oldest. |
No output schema declared.
No examples provided.
list_estimators ~34
List the built-in estimator families this server knows about. Useful for picking a model alias when the exact model name isn't recognized.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
What is the io.github.MukundaKatta/agentfit MCP server?
io.github.MukundaKatta/agentfit is an MCP server listed in the public MCP registry as io.github.MukundaKatta/agentfit. Token-aware message truncation: fit a chat history into your model's context budget. This page covers its npm package (@mukundakatta/agentfit-mcp).
Is the io.github.MukundaKatta/agentfit MCP server safe to use?
io.github.MukundaKatta/agentfit scores 83 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 22 September 2026. It declares no install or post-install scripts. 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.MukundaKatta/agentfit MCP server expose?
io.github.MukundaKatta/agentfit exposes 3 tools: count_tokens, fit_messages, list_estimators. Their descriptions and schemas cost roughly 359 tokens of context every time the server is loaded.
Is the io.github.MukundaKatta/agentfit MCP server still maintained?
io.github.MukundaKatta/agentfit is still listed as active in the MCP registry. We last reached this channel on 22 September 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.MukundaKatta/agentfit MCP server under?
io.github.MukundaKatta/agentfit declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.