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aminamos

aisafety-mcp

by aminamos

search_media_channels

Query the AISafety.com directory to find podcasts, newsletters, blogs, and video channels on AI safety. Use free-text search and type filters to get relevant results.

Instructions

Podcasts, newsletters, blogs, and video channels. Query https://aisafety.com/api/v1/media-channels. 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.
typeNoFilter by type (case-insensitive substring; comma-separate for OR).
limitNoMax rows to return (1-50, default 10).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.5/5.0
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 and mostly fails it: nothing about auth, rate limits, pagination, or return shape. The only behavioral facts given are the data source URL and the CC-BY-4.0 attribution requirement, which is genuinely useful but far from sufficient for a search endpoint.

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?

It is short and front-loaded with the resource types, but two of three sentences are spent on the API URL and licensing rather than on helping the agent invoke the tool. The raw URL also risks being read as an instruction to call the endpoint directly.

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

Completeness2/5

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

With no output schema and no annotations, the description must explain return format, result volume, and search semantics — none of which appear. The attribution/licensing note is a plus, but a three-parameter search tool across ten siblings needs more than a resource list to be callable with confidence.

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 coverage is 100% and all three parameters (q, type, limit) are fully documented in the schema, including the 1-50 limit range and default of 10. The description adds no parameter meaning beyond that, so the baseline of 3 applies.

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

Purpose3/5

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

The description enumerates the resource types (podcasts, newsletters, blogs, video channels) but never states the verb — that it searches or lists them. It relies on the tool name for the action, and it does not distinguish itself from any of its ten search_* siblings beyond the differing dataset.

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 guidance on when to use this tool versus search_organizations, search_events, or the other siblings, and no advice on how to drive the free-text query. The only quasi-usage note is a license/attribution request, which is not a selection criterion.

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