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Longhand

by Wynelson94
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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match_project

Resolve ambiguous project references by fuzzy matching partial names, categories, or descriptions to return candidate projects with match reasons.

Instructions

Fuzzy project matching. Given a partial project name, category, or description, returns candidate projects with match reasons. Useful for confirming 'which game did you mean?' before drilling into episodes.

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": "match_project"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
top_kNoMax project matches to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

No annotations provided, so description bears full burden. It describes a read-like operation (returns candidates) but does not explicitly state it is non-destructive or safe. Could be more transparent about side effects.

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

Conciseness5/5

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

Two sentences with no unnecessary words. First sentence immediately states the primary function, making it easy for an agent to parse.

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

Completeness4/5

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

No output schema exists, but description mentions 'match reasons' as part of output, giving a clue. For a simple fuzzy match tool, this is sufficient; however, more detail on return structure could be helpful.

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

Parameters4/5

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

Schema description coverage is 50% (top_k described, query not). Description adds value by explaining 'query' can be a partial name, category, or description, which enriches the schema's bare parameter definition.

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

Purpose5/5

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

Clearly states it performs fuzzy matching on partial project name, category, or description, returning candidates with match reasons. Distinguishes from siblings like list_projects (exact listing) and search (broader search).

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?

Provides the use case of disambiguation before drilling into episodes, which gives clear context. Does not explicitly state when not to use or name alternatives, but the intended scenario is well-defined.

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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