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openpitch-mcp

openpitch-mcp

by Avierovich
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_companies

Read-onlyIdempotent

List AI companies with key metrics like ARR and funding. Filter by segment or sort by rank to find relevant startups.

Instructions

List covered AI companies with headline metrics.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_companies"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
filterNo
segmentNoall
sort_byNouniverse_rank

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds minimal behavioral context beyond 'headline metrics'. It does not mention return format, pagination, or data freshness, but the safety profile is clear from annotations.

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

Conciseness3/5

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

The description is very short and front-loaded, but too terse. It omits critical details, making it minimally adequate but not efficient for agent decision-making.

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

Completeness2/5

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

Given 4 optional parameters with no output schema and multiple sibling tools, the description lacks necessary context about parameter behavior, return values, and differentiation from similar tools. The brevity leaves the agent underinformed.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not explain any of the 4 parameters (limit, filter, segment, sort_by). Without additional text, the agent has no guidance on parameter meaning, format, or valid values beyond defaults.

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 clearly specifies the verb 'List' and resource 'covered AI companies' with 'headline metrics', distinguishing it from siblings like 'get_company' (single company) and 'compare_companies' (comparison). However, it does not explicitly differentiate from 'search' or 'what_moved', leaving some ambiguity.

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 provides no guidance on when to use this tool versus siblings. No mention of prerequisites, exclusions, or context for appropriate usage, leaving the agent to infer from the name and sibling list alone.

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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