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Job Spotter: How relevant a connection is to your goal

jobspotter_connection_score
Read-onlyIdempotent

Job Spotter. How relevant a connection is to your goal. For "should I connect with this person?", "is this contact worth messaging about my job search?". Scores how relevant a connection is to the user's goal, out of 100, from two profiles the user provides (it rates the fit with the goal, not the person), the same way every time: works where they want to work, recruiter or likely hiring manager for the goal, same school, same past employer, shared skills, nearby, more senior. Gives the reasons, a short verdict and an outline for a short note. Profiles can be pasted text (for example from LinkedIn's Save to PDF). Nothing sent is stored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
meYesA profile: text, and or fields title, company, employers, schools, location, skills.
goalNoWhat the person is aiming for, for example "data analyst at Monzo".
themYesA profile: text, and or fields title, company, employers, schools, location, skills.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / me / description
      Previous value: -"A profile: text, and or fields title, company, past_companies, schools, location, skills."New value: +"A profile: text, and or fields title, company, employers, schools, location, skills."
    • changedInput schema / properties / them / description
      Previous value: -"A profile: text, and or fields title, company, past_companies, schools, location, skills."New value: +"A profile: text, and or fields title, company, employers, schools, location, skills."
  2. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world. The description adds value beyond them by disclosing determinism ('the same way every time'), a privacy guarantee ('Nothing sent is stored'), and the shape of the response (reasons, short verdict, note outline) despite no output schema.

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?

Purpose is front-loaded, but the middle is a dense run-on stacking parentheticals and a seven-item factor list, plus it partly restates the name and title. It is understandable but not tightly edited.

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?

With no output schema, the description usefully sketches the return (reasons, verdict, note outline), and it covers privacy and determinism. The nested two-object input is referenced but the description leans on the schema for the field-level detail, which is acceptable given full schema coverage.

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%, so the schema already documents the me/them profile fields and the goal. The description adds only marginal meaning — that profiles may be pasted free text (e.g., LinkedIn Save to PDF) and that two profiles are provided — so baseline 3 is appropriate.

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?

States a specific verb and resource — it scores how relevant a connection is to the user's goal — and adds the output scale (out of 100) and the exact inputs (two user-supplied profiles). It also explicitly disambiguates scope: 'it rates the fit with the goal, not the person.' An agent can distinguish this from find_people or referral_note without opening a 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 opening quotes the user questions it answers ('should I connect with this person?', 'is this contact worth messaging about my job search?'), which gives clear usage context. It does not explicitly name an alternative sibling (e.g., referral_note for actually writing the note), so it stops short of full when-not/alternative routing.

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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