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mcp-route-master

mcp-route-master

by CoderShivamGusain
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Search route-master

search
Read-onlyIdempotent

Finds intercity bus routes, official booking links, RTC operator details, and pay-safely checklist entries in India; returns JSON results to open with fetch.

Instructions

Search route-master's knowledge: bus routes (booking links), the pay-safely checklist and government (RTC) operators. Returns JSON {"results": [{"id", "title", "url"}]}; open one with fetch. Standard search tool for ChatGPT connectors and deep research.

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"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesWhat to look for, e.g. 'Delhi to Manali tomorrow', 'HRTC', 'refund scam'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive) and openWorldHint=false, and the description is consistent with that by scoping results to "route-master's knowledge" rather than the open web. With no output schema, the description adds real value by spelling out the returned JSON structure and the required next step. It stops short of noting result counts or pagination limits, so it is not fully complete.

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?

Three compact sentences, front-loaded with the corpus scope, followed by the return contract and the next action. No filler or restatement of the title.

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 one-parameter, non-destructive lookup on a bounded corpus, the definition supplies what an agent needs: what is searched, what comes back, and what to do with a hit. The only gap is any statement about result volume or ranking limits, which is minor here.

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 100% and the single query parameter already carries concrete examples ('Delhi to Manali tomorrow', 'HRTC', 'refund scam'). The description adds no further syntax, length, or phrasing guidance beyond what the schema states, so this sits at the documented baseline.

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 names a specific verb (search) and scopes the resource precisely: bus routes with booking links, the pay-safely checklist, and government (RTC) operators. It also declares the return shape ({"results": [{"id", "title", "url"}]}) and the follow-up verb (open one with fetch), which distinguishes it from siblings like check_operator or plan_bus_trip.

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?

It frames itself as the general entry point ("Standard search tool for ChatGPT connectors and deep research") and explicitly routes to the fetch sibling for opening a result. There is no explicit when-not guidance, but the sibling set is differentiated enough that an agent can infer search-then-fetch without ambiguity.

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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