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fletcher-agent

fletcher-agent

Official
by FletcherAgent
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

search_token

Look up a token in the agent signal database to retrieve its confidence score, verdict, enrichment data, and buy/skip status.

Instructions

Search for a token in the agent signal database. Returns last seen confidence, verdict, enrichment data, and whether the agent bought or skipped it

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": "search_token"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesToken symbol or mint address to search

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.5

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description must fully convey behavioral traits. It states the return data (last seen confidence, verdict, enrichment, bought/skipped), implying a read-only search operation. However, it does not explicitly confirm side effects (e.g., mutability), auth requirements, or rate limits. While the return description is helpful, it lacks depth on behavior.

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 a single sentence, which is concise, but it packs multiple return items into a run-on structure. It is front-loaded with the action and result, making it efficient. However, minor structural improvement could enhance readability.

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?

Given the tool's simplicity (single parameter, no output schema, no annotations), the description provides a basic understanding of input and output. However, it lacks details on search behavior (e.g., partial match, case sensitivity) and error cases, leaving gaps for a complete understanding.

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?

The schema covers 100% of parameters with a description for 'query' ('Token symbol or mint address to search'). The tool description adds no new meaning beyond this. Per the scoring guide, high schema coverage warrants a baseline of 3, and the description does not compensate further.

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?

The description clearly states the tool's purpose: 'Search for a token in the agent signal database.' It specifies the resource (token in database) and the action (search). The stated return fields (confidence, verdict, etc.) further clarify the purpose. This distinguishes it from sibling tools like 'get_agent_dna' or 'get_trade_history' which focus on other data.

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 does not provide any guidance on when to use this tool over alternatives, nor does it mention prerequisites, limitations, or exclusions. Without such context, an AI agent may not know when this tool is appropriate or when to prefer a sibling tool.

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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