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environ-mcp

environ-mcp

by renhail
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

按关键字搜索站点

search_stations

Searches environmental monitoring stations the current user can view by keyword, matching name, code, parent station, or address, and returns station IDs for detail queries.

Instructions

按关键字搜索当前用户有权限查看的环境监测站点。关键字可匹配站点名称、站点编码、上级站点名和站点地址（含城市、区县）。⚠️ 当用户只说了模糊的地名或行业词（如「污水」「重庆」「兰州」）而不确定是哪个站点时，先用这个工具搜出候选站点，拿到站点 ID 后再调用 get_overview / get_realtime / get_hour_data。

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": {
      "keyword": "<keyword>"
    },
    "name": "search_stations"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo返回条数，默认 20
keywordYes搜索关键字：站点名称的一部分、站点编码，或地名

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.5

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden, and it does cover the important traits: the result set is limited to stations the caller can see, and matching is fuzzy across several fields. It says nothing about behaviour on zero matches, result ordering, or what the returned records contain, which matters for a tool whose output feeds downstream calls.

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: purpose and match scope first, then the conditional workflow warning. Nothing is padded, and the warning is placed where it will be read before the agent picks a tool.

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?

For a two-parameter search with no output schema and no annotations, the description supplies the essential context, including the fact that results carry station IDs that downstream tools need. The remaining gap is what a returned record looks like and how to handle no-hit or over-limit cases.

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 coverage is 100%, so the baseline is 3, but the description genuinely adds meaning beyond the schema by expanding what the keyword matches to include 上级站点名 and 站点地址 (含城市、区县), well past the schema's shorter '名称的一部分/编码/地名'. Only limit goes unmentioned, and its default is documented in the schema.

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 uses a concrete verb and resource ('按关键字搜索…环境监测站点') and adds permission scoping plus the exact fields the keyword matches (name, code, parent name, address). It never distinguishes itself from the closest sibling, list_stations, so an agent still has to guess which of the two enumeration tools to pick.

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

Usage Guidelines5/5

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

It gives an explicit trigger ('当用户只说了模糊的地名或行业词' with concrete examples 污水/重庆/兰州) and an explicit next step: take the returned 站点 ID into get_overview / get_realtime / get_hour_data. The alternative tools are named together with the condition that selects them, leaving nothing for the agent to infer.

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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