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TMF (True Memory Fragments)

TMF (True Memory Fragments)

by kyle641320
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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tmf_explain

Explain one claim's source-linked context, flagging partial coverage and stale sources that degrade to authoritative references. Set full=true for body details.

Instructions

Explain one claim; full=true includes thick body/source-bound details. 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": {
      "claim_id": "<claim_id>"
    },
    "name": "tmf_explain"
  }
}

Input Schema

NameRequiredDescriptionDefault
fullNo
claim_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It usefully states that coverage is partial, freshness does not equal correctness, the source is authoritative, and stale results should degrade to source, but it omits operational details such as side effects, permissions, or return format.

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?

The description is short and front-loaded, stating the core action before the flags and caveats. The semicolon-separated shorthand is efficient, though phrases like 'fresh != correct' are slightly cryptic.

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 simple two-parameter tool with no output schema, the description covers the main purpose, the 'full' flag, and data-quality caveats. However, it does not explain return values or when to prefer this tool over siblings, leaving gaps an agent might need to resolve.

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% for two parameters. The description adds meaning for 'full' by saying it includes thick body/source-bound details, and 'claim_id' is implicit from 'Explain one claim', but the identifier's format or constraints are not described.

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?

The description states a specific verb and resource: 'Explain one claim', and adds what the 'full' mode includes. It clearly identifies the operation but does not differentiate itself from sibling tools such as tmf_retrieve or tmf_context. This meets the definition of a clear purpose without sibling differentiation.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives like tmf_retrieve or tmf_context. It only explains a parameter-level option ('full=true includes thick body/source-bound details') and offers data caveats, not usage conditions or exclusions.

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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