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Casius999

decroche-mcp

by Casius999

recruiter_identify

Identify recruiters by parsing pasted text from LinkedIn profiles, email signatures, or company team pages. Operates only on the provided content.

Instructions

Parse a user-pasted text block to identify a recruiter.

Operates ONLY on the provided text — never fetches any URL. Accepts LinkedIn profile text copy-pasted by the user, email signatures, or company "team" page text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesRaw pasted text.
target_companyNoOptional target company name for kind classification.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
titleNo
companyNo
kindNounknown
sourceNopasted
linkedin_urlNo
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool never fetches URLs and only works on the provided text. It also lists acceptable input formats. However, it does not describe the return value or any potential side effects, but given the read-only nature and output schema exists, this is adequate.

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?

The description is three short, focused sentences with key constraints upfront. Every sentence adds essential information without filler. It is optimally concise for an agent.

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 complexity (parsing text to identify a recruiter), the description sufficiently covers purpose, input constraints, and acceptable content. The output schema exists but is not described here, which is acceptable as per guidelines. It provides enough context for correct invocation.

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% with both parameters described. The description adds value by explaining that target_company is for 'kind classification,' which goes beyond the schema's 'Optional target company name.' This enhances understanding.

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?

Description clearly states 'Parse a user-pasted text block to identify a recruiter.' It uses a specific verb and resource, and distinguishes itself from sibling recruiter tools like recruiter_draft_message or recruiter_find_contact by focusing on parsing pasted text rather than contacting or qualifying.

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 specifies that it operates only on provided text, never fetches URLs, and lists acceptable input types (LinkedIn profile text, email signatures, company team pages). It clearly indicates when to use this tool, though it does not explicitly mention when not to use alternatives.

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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