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TMF (True Memory Fragments)

TMF (True Memory Fragments)

by kyle641320
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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tmf_retrieve

Query thin, source-linked TMF claims to investigate a codebase before reading whole files. Use it to find relevant code claims and detect stale context; source remains authoritative.

Instructions

Investigating a codebase: start here; usually cheaper than grep plus whole-file reading. Retrieve thin TMF claims for a lexical query. Partial coverage; fresh != correct; source is authoritative; stale should degrade to source.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "tmf_retrieve"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It usefully discloses accuracy caveats — 'partial coverage; fresh != correct; source is authoritative; stale should degrade to source' — which is real behavioral value, but nothing about result shape, cost of the limit, or pagination behavior is given.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Very compact, front-loads the 'start here' guidance, and every clause carries information. The telegraphic phrasing ('fresh != correct', 'stale should degrade to source') is dense but readable, not padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, no annotations, and 0% schema coverage, the description covers accuracy/trust caveats well but leaves gaps: what a 'thin TMF claim' looks like in the response, and the meaning of the limit parameter are not addressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema documents nothing. The description compensates partially by specifying the query is 'lexical' (a meaningful constraint not in the schema), but the `limit` parameter (1-50) is never explained or bounded in prose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: 'Retrieve thin TMF claims for a lexical query', and positions itself as the entry point for codebase investigation. It also distinguishes itself from grep as an alternative approach, though it doesn't differentiate from close siblings like tmf_fragment or tmf_context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Investigating a codebase: start here' and 'usually cheaper than grep plus whole-file reading' give clear context for when to reach for this tool, plus a fallback rule ('stale should degrade to source'). It stops short of naming when a sibling such as tmf_fragment or tmf_context is preferable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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