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aminamos

aisafety-mcp

by aminamos

search_jobs

Find open AI safety roles by searching across all fields, filtering by location, role type, organization, or experience level, with adjustable result limits.

Instructions

Open roles in AI safety. Query https://aisafety.com/api/v1/jobs. Data: AISafety.com, licensed CC-BY-4.0 — please credit AISafety.com when using this data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text search across all fields.
limitNoMax rows to return (1-50, default 10).
locationNoFilter by location (case-insensitive substring; comma-separate for OR).
roleTypeNoFilter by roleType (case-insensitive substring; comma-separate for OR).
organizationNoFilter by organization (case-insensitive substring; comma-separate for OR).
workLocationNoFilter by workLocation (case-insensitive substring; comma-separate for OR).
minimumExperienceNoFilter by minimumExperience (case-insensitive substring; comma-separate for OR).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the data source and CC-BY-4.0 licensing/attribution expectation, which is genuinely useful, but says nothing about read-only semantics, pagination behavior, rate limits, or whether all fields are always populated.

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?

Very short and front-loaded: the resource comes first, followed by the endpoint and licensing note. The license boilerplate is compliance-relevant rather than selection-relevant, but each fragment is brief enough to be justified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 0 required parameters and a fully documented schema, an agent can invoke this correctly without more guidance. The gap is return-value shape: there is no output schema and the description never hints at what fields a job record contains, so the agent cannot anticipate results.

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% and every filter (location, roleType, organization, workLocation, minimumExperience, q, limit) is documented in the schema with matching-case semantics. The description adds no parameter nuance beyond what the schema already provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the resource precisely ("Open roles in AI safety") and the underlying API endpoint, so an agent knows this returns job postings. It does not, however, distinguish itself from the many sibling search_* tools beyond the resource name, which the tool name already conveys.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool, when not to, or which sibling to prefer for adjacent needs (e.g. search_organizations vs. search_jobs when seeking employer info). Usage must be inferred entirely from the name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.