Evaluate one candidate in depth — full skills, summary, seniority, experience (titles + what they did) and education. Stays ANONYMOUS: no name, no contact, and employer names, dates, and graduation years are withheld to protect identity. Counts toward your hourly/daily reveal limit. To actually reach someone, call request_contact.
| Name | Required | Description | Default |
|---|---|---|---|
| resume_id | Yes | ||
| candidate_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds substantial behavioral context: it stays anonymous (no name, no contact, withheld employer names/dates/graduation years) and counts toward the hourly/daily reveal limit. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long, with the primary action front-loaded in the first sentence. It includes only essential details (evaluation scope, anonymity, reveal limit, contact alternative) with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the evaluation content, anonymity constraints, rate-limit implications, and the contact alternative, making it sufficiently complete for an AI agent to understand the tool's role. The only notable gap is parameter semantics, which is already scored separately, so overall context remains strong given the annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has two required parameters (candidate_id, resume_id) with zero description coverage, and the tool description does not explain what these IDs mean, how they differ, or their expected format. Given the 0% schema coverage, the description needed to compensate but failed to do so.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Evaluate' and identifies the resource as 'one candidate in depth', enumerating the fields covered (skills, summary, seniority, experience, education). It also distinguishes itself from siblings by explicitly stating it stays anonymous and directing users to request_contact for actually reaching someone.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly names request_contact as the alternative when the goal is to reach someone, and it notes the reveal limit, which implies usage context. However, it does not explicitly contrast with list-oriented siblings like search_candidates, so the guidance is clear but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct operation: subscription, candidate search, detailed view, contact management, stats, docs, new candidates, watchlist, outcome reporting, and saving. No two tools overlap in purpose; the descriptions clearly differentiate them.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_subscription, get_candidate, search_candidates). The verbs vary as needed for different actions, maintaining a predictable structure.
With 11 tools, the set is well-scoped for a candidate sourcing platform. It covers search, detailed viewing, contact, watchlist management, subscription, and documentation without being excessive or too sparse.
Core workflows are covered: search, view, request contact, report outcome, save to watchlist. Minor gaps exist, such as lacking a remove-from-watchlist tool or a way to cancel a contact request, but these do not severely hinder the main use case.