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job-search-mcp

Evaluate Jobs

evaluate_jobs

Score each sourced job for a candidate with a 0-100 fit score, considering must-have requirements, skills, and domain fit. Returns job_id, score, and reason.

Instructions

Score one or more sourced jobs for THIS candidate (holistic). YOU assign each job a 0-100 fit score directly, using the candidate summary (shown in find_jobs / whats_promising) and the job description. Weigh the must-have requirements, the candidate's actual skills and years of experience (a role demanding far more seniority/years than the candidate has should score LOWER), domain fit, and standout strengths. Calibrate and use the FULL range — do not bunch everything at 80+: 80-100 = strong fit and realistic; 60-79 = good with real gaps; 40-59 = partial/stretch (e.g. wrong level); below 40 = poor or wrong field. Pass { job_id, score, reason } per job. Use the EXACT bracketed ids; do not invent them. Score every job in one call. Returns text; call show_board afterward to display the ranked board.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
evaluationsYesOne entry per job to score.
Behavior4/5

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

No annotations provided; description discloses the scoring behavior, range usage, and return type ('Returns text'). However, does not explicitly state if it modifies state or requires permissions.

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?

Moderate length but well-structured with clear instructions, front-loaded purpose, and every sentence adds value. Slightly wordy but necessary for complex scoring guidelines.

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?

Covers the scoring criteria, calibration, and post-call step. Lacks explicit return format details, but overall complete given the complexity and no output schema.

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 good field descriptions. Description adds contextual meaning about scoring logic, calibration, and post-call actions, going beyond schema details.

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?

Clearly states it scores one or more sourced jobs for a candidate, assigning 0-100 fit scores. Distinct from siblings like find_jobs (finding) and show_board (displaying).

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?

Explicitly says to use after finding jobs ('sourced jobs'), instructs to score all jobs in one call, and recommends calling show_board afterward. Also provides calibration advice.

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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