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Glama

YouSpot

by com.youspot

Find companies

find_companies
Read-only

Search for companies by describing them in plain English — the same engine behind the ProspectFinder app. Handles industry, size, location, funding stage and investors in one query: 'CRM software companies with more than 1,000 employees', 'Series A AI startups in Boston', 'YC-funded fintechs'. Returns name, domain, employee count and description per company. Use this for discovering companies out in the world; for companies/people the user already knows, use their graph and connection tools instead. Results can then be added to the user's graph with the graph tools if they ask. The result includes a result_id that chat surfaces can use to render the list as a live prospect_list card.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax companies to return (default 10, max 25).
queryYesPlain-English description of the companies to find (industry, size, location, funding, investors).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, so the safe read nature is already known. The description adds valuable behavioral context: it uses the ProspectFinder engine, handles multiple criteria in a single query, returns specific fields, and includes a result_id for rendering live prospect_list cards. No contradiction with annotations.

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 moderately long but every sentence earns its place: functionality, examples, return fields, usage boundaries, and result_id handling. It is well structured with no redundancy. Slightly dense but appropriate for the tool's complexity.

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

Completeness5/5

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

Given there is no output schema, the description fully compensates by specifying the return fields (name, domain, employee count, description) and the result_id behavior. It also covers input semantics, usage context, and post-processing steps, leaving no critical gap for an agent to invoke the tool correctly.

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 100%, covering both query and limit clearly. The description adds extra semantic value by illustrating the plain-English query format with three concrete examples and listing the dimensions it handles. Limit semantics are left to the schema, which is sufficient.

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 uses a specific verb and resource: 'Search for companies by describing them in plain English.' It clarifies the scope (discovering companies 'out in the world') and differentiates itself from sibling tools by explicitly stating the alternative for known companies/people. Concrete examples make the tool's behavior unmistakable.

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?

The description states exactly when to use this tool ('for discovering companies out in the world') and when not to ('for companies/people the user already knows, use their graph and connection tools instead'). It also mentions the follow-up action of adding results to the graph, giving the agent a clear workflow.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, with clear boundaries even within overlapping domains like LinkedIn (search vs. free-form query vs. profile vs. summary) and graph deletion (soft single, bulk soft, permanent single). Descriptions explicitly cross-reference related tools to prevent misselection.

Naming Consistency4/5

The vast majority follow a consistent verb_noun pattern (get_, list_, search_, create_, delete_, etc.). A few noun-phrase exceptions like linkedin_analytics, mutual_connections, similar_objects, and what_needs_attention deviate slightly, but they are still descriptive and do not create confusion.

Tool Count2/5

At 66 tools this is far beyond the 25+ threshold considered too many, even though the server covers many integration domains. Each domain has a coherent subset, but the overall surface is heavy for agents to navigate and would benefit from consolidation or namespacing.

Completeness4/5

The set provides deep read/search coverage across Gmail, Slack, Calendar, LinkedIn, HubSpot, Obsidian, Twitter, and a graph store, with core write operations for calendar, drafts, Slack, and graph objects. Minor gaps exist—notably no calendar delete, no direct Gmail send to third parties (only drafts), and no LinkedIn post/message actions—but these appear deliberate and do not block typical workflows.

Resources