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BACH-AI-Tools

Fresh Linkedin Profile Data MCP Server

Search Companies Instantly

search_companies_instantly

Search a cached database of 64 million company records for matching companies. Each search consumes 1 credit and returns up to 10 results.

Instructions

Search companies in our cached database of 64 million records (updated annually). Instant results. Cost: 1 credit for 10 companies (searches returning fewer than 10 results still consume 1 credit).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv2.0.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the cached nature, annual update cadence, instant performance, and a precise credit-cost rule including the edge case for fewer than 10 results. It does not mention rate limits or authorization, but for a read-only search tool the key behaviors are covered.

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 sentences, all informative and front-loaded. The first sentence states the core action and data source, the second adds performance, and the third specifies cost. No filler or repetition.

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?

Given the tool's simplicity (0 params, no output schema, no annotations), the description is mostly sufficient: it covers source, freshness, speed, and cost. The main gap is that it does not state what the response looks like or how the search query is determined without parameters, though these may be implicit in the tool's design.

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?

The input schema has zero parameters, so the baseline for 0 params is 4. The description adds no parameter-level meaning because none is needed; there is nothing to document beyond what the schema already shows.

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 states a specific verb ('Search'), a clear resource ('companies'), and the distinctive data source ('cached database of 64 million records updated annually'). The 'Instant results' line separates it from slower search siblings, so an agent can tell it apart from search_companies without opening another schema.

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?

The description provides clear context: use this when you need instant results from cached data. It does not explicitly name alternatives or state when not to use it, but the contrast between 'instant' and the sibling search tools implies a speed-based choice.

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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