Skip to main content
Glama
aminamos

aisafety-mcp

by aminamos

search_funding

Find funders and grant programs for AI safety work. Search by keyword, type, recipient, and whether applications are open.

Instructions

Funders and grant programs for AI safety work. Query https://aisafety.com/api/v1/funding. 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).
recipientTypeNoFilter by recipientType (case-insensitive substring; comma-separate for OR).
acceptingApplicationsNoFilter by acceptingApplications (case-insensitive substring; comma-separate for OR).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and discloses the data source, license, and an attribution obligation — genuinely useful behavioral context. However, it says nothing about whether the operation is read-only, result size limits, pagination, or return shape, leaving meaningful gaps for a no-annotation tool.

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?

Three compact sentences with the resource front-loaded; nothing is padded. The API URL is arguably low-value for an agent, but the licensing note is a legitimate operational detail.

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?

For a five-parameter tool with no output schema and no annotations, the description covers the data source and licensing but omits what a result row contains, default behaviors, and any usage context. Combined with the fully documented schema it is adequate, but not complete.

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 all five filters (q, type, limit, recipientType, acceptingApplications). The description adds no syntax, format, or interaction detail 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.

Purpose4/5

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

The description names the specific resource ('Funders and grant programs for AI safety work'), which distinguishes it from siblings like search_events and search_jobs, though the search verb itself is only implied by the tool name. It is clear and specific about the domain, but does not explicitly contrast itself with any alternative.

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 when-to-use guidance, no mention of siblings or exclusion conditions, and no indication of when this tool is preferable to alternatives. The agent must infer usage entirely from the tool name.

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