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Find a free, ready-made AI agent for a task in the open agent registry (RAR).

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Status
Healthy
Last Tested
Transport
Streamable HTTP · MCP 2025-06-18
URL
Repository
airaptr/airaptr.github.io
GitHub Stars
0

TDQS

A3.9/5.0

Scored across 5 tools

Disambiguation4/5

The five tools mostly target distinct steps: search (find_agents), retrieve code (get_agent_code), run in chat (use_agent_here), run locally (how_to_run_agent), and fallback request (request_service). However, use_agent_here references a nonexistent 'check_agent' tool, and the two running tools could be confused without reading the detailed descriptions.

Naming Consistency3/5

Most names follow a verb_noun snake_case pattern (find_agents, get_agent_code, request_service, use_agent_here), but how_to_run_agent breaks the convention with an interrogative phrase. The set is still readable but not uniformly predictable.

Tool Count5/5

Five tools is well-scoped for an agent registry finder and runner. Each tool maps to a clear workflow step (search, inspect, run, persist, escalate), so none feels redundant or missing.

Completeness3/5

The core find-retrieve-run-keep-request lifecycle is covered, but the surface is incomplete because use_agent_here explicitly references a check_agent step that has no corresponding tool. This creates a dead end for safety or validation workflows.

Available Tools

5 tools
find_agentsFind existing agentsA
Read-only
Inspect

Searches the open agent registry (RAR) (RAR, about 1,700 single-file agents) for agents that already do what the user wants, for example when they ask 'is there an AI tool for...' or want a ready-made automation instead of building one. Use it before building from scratch, or when the user asks whether an agent exists for a task.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoHow many results, default 5
queryYesPlain words describing the task, e.g. 'summarize sales calls'

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint, so the safety profile is covered. The description usefully discloses registry scope and size (open, ~1,700 single-file agents), but says nothing about rate limits, result freshness, or what a match actually returns. Modest added value over the structured hints.

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?

Two compact sentences with the core purpose front-loaded; every clause does work. Minor redundancy in restating 'RAR' twice back-to-back ('(RAR) (RAR, about 1,700...)'), which is slightly sloppy but not costly.

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 discovery tool with no output schema, the description should hint at what matches look like (agent name, description, code reference) since the agent must decide what to do next with results. It covers when to call well but leaves the return shape and follow-up path (e.g., get_agent_code) unstated.

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 both parameters are already documented, including the example 'summarize sales calls' for query. The description reinforces the query intent with example user utterances but adds no format or semantics beyond the schema, so baseline 3 is right.

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?

States a specific verb and resource ('Searches the open agent registry (RAR)') and scopes it precisely ('about 1,700 single-file agents'). The framing as finding 'agents that already do what the user wants' lets an agent distinguish it from siblings like get_agent_code or use_agent_here without opening a schema.

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

Usage Guidelines4/5

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

Gives explicit triggers: 'Use it before building from scratch, or when the user asks whether an agent exists for a task,' plus sample phrasings like 'is there an AI tool for...'. It does not name alternative tools for when the user already knows an agent by name, so it stops short of full alternative routing.

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

get_agent_codeGet an agent's codeA
Read-only
Inspect

Returns the full source of one agent from the registry, by its name (e.g. @bill/neuron_agent), so it can be read or adapted.

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesRegistry name, e.g. @kody/memory_agent

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description usefully adds that the whole source is returned rather than metadata, but says nothing about behavior when the name is unknown or about any size limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence with zero waste, front-loading the return value and the lookup key before the supporting example. Nothing needs trimming.

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?

With no output schema, the description carries the burden of describing the return, and 'full source' covers it adequately for a single-parameter read tool. Minor gaps are error behavior and whether output is raw text or structured.

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 the single 'name' parameter is fully documented in the schema with an example. The description's parenthetical example (@bill/neuron_agent) reinforces the format but adds no semantics beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource (returns the full source of one agent from the registry), names the lookup key (its name), and gives the exact identifier format. An agent can tell this apart from get_agent_template and check_agent without opening any schema.

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

Usage Guidelines3/5

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

The phrase 'so it can be read or adapted' implies the use case but never states when to choose this over siblings like get_agent_template, find_agents, or check_agent. Guidance is implied rather than explicit, with no exclusions.

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

how_to_run_agentKeep an agent running on your computerA
Read-only
Inspect

Optional, for later: how to keep an agent running on the user's own computer with the free Brainstem. Only offer this after the user has used the agent in the chat and wants to keep it.

ParametersJSON Schema
NameRequiredDescriptionDefault
osNoThe user's operating system
filenameNoThe agent file name, if known

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the prerequisite gating and the 'free Brainstem' framing, but says nothing about what the tool returns or how the guidance is delivered, so it adds only moderate context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no waste; the optional/later framing and the prerequisite are front-loaded where an agent will see them before deciding to offer the tool.

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?

For a read-only instructional tool with two optional params and no output schema, the description covers the essential decision context. It could note that the result is guidance text to relay to the user, but nothing needed to call it correctly is missing.

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%, with both optional params (os enum, filename) fully documented in the schema. The description adds no syntax or format detail for either parameter, so the baseline 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 a specific resource and scope: instructions for keeping an agent running on the user's own computer via the free Brainstem. It implicitly distinguishes itself from siblings like use_agent_here and get_agent_code by being the local-hosting guide, though it never names those siblings explicitly.

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

Usage Guidelines4/5

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

Gives a clear timing condition ('Optional, for later') and a prerequisite ('Only offer this after the user has used the agent in the chat and wants to keep it'). It does not name alternative tools for related needs, so it stops short of a 5.

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

request_serviceAsk us to build somethingAInspect

Use when the person wants something none of these tools can do and says yes to passing the request on. Records only the request text they agree to send (no name or contact). Ask before calling it.

ParametersJSON Schema
NameRequiredDescriptionDefault
requestYesWhat they want, in a sentence, as they agreed to send it

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare a non-read-only, non-destructive, closed-world write, and the description adds meaningful context beyond that: only the request text is stored, no name or contact data, and user consent must be obtained first. It stops short of describing what happens after the request is recorded or any confirmation behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the usage condition, then the privacy scope, then the consent gate. Nothing is redundant and every sentence carries actionable information.

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?

For a single-parameter write tool with no output schema, the description covers trigger, consent, and data scope adequately. Only minor gaps remain, such as what the agent should do if the person says no or what happens after the request is recorded.

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 the single parameter's description already says 'as they agreed to send it.' The description's phrasing mirrors that constraint rather than adding format, length, or fidelity details beyond the schema, so baseline 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 tool records a request the person agrees to send, which is a specific verb+resource, and implicitly distinguishes it from siblings by framing it as the fallback when none of 'these tools' can fulfill the need. It is clear, though the purpose is conveyed through usage framing rather than a direct statement.

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 gives the trigger condition ('wants something none of these tools can do and says yes to passing the request on') and a prerequisite ('Ask before calling it'). The agent knows exactly when to invoke it and what consent gate must be cleared first.

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

use_agent_hereUse the agent in this chatA
Read-only
Inspect

Use this right after an agent passes check_agent, or whenever the user wants to try an agent. Returns a short Python runner so you can run the agent in this chat with your own Python tool on the user's own data (pasted text, an uploaded spreadsheet, a list). Nothing to install. If Python is unavailable, apply the agent's logic yourself by reading its code. Ask the user for their real data, run the agent, and show the result in plain words.

ParametersJSON Schema
NameRequiredDescriptionDefault
filenameNoThe agent file name, e.g. invoice_triage_agent.py

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered and the description does not contradict it. The description adds real behavioral context beyond the annotations: what is returned (a short Python runner), that nothing needs installing, and the no-Python fallback. It omits execution/return-format details of the runner itself, so not a 5.

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?

The description is front-loaded with the primary trigger and the primary outcome, then adds the fallback instruction. Sentences are dense but each carries information; the 'Ask the user for their real data, run the agent, and show the result' sentence is slightly prescriptive/duplicative but still actionable.

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?

With one optional parameter, full schema coverage, no output schema, and annotations covering the safety profile, the description covers what an agent needs: when to invoke, what it returns, and what to do without Python. Naming the sibling check_agent as the upstream step closes the main routing gap; only the exact usage of the returned runner is left implicit.

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?

There is only one parameter (filename) and schema description coverage is 100%, so the schema already documents its meaning and expected format ('invoice_triage_agent.py'). The description adds no further semantics about the filename (e.g. where it comes from or whether it must match the checked agent), 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 a specific outcome: it 'Returns a short Python runner so you can run the agent in this chat with your own Python tool on the user's own data.' This clearly distinguishes it from siblings like check_agent and get_agent_code by naming the concrete artifact returned. It stops short of a 5 because the verb ('use') is generic and the differentiation from get_agent_code/how_to_run_agent is only implied.

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

Usage Guidelines4/5

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

It gives explicit triggering conditions ('right after an agent passes check_agent, or whenever the user wants to try an agent') and names check_agent as the upstream step. It also supplies a fallback path ('If Python is unavailable, apply the agent's logic yourself'). No explicit 'do not use this when...' exclusion against other siblings, so not a 5.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 5 tool updates
    • First observedfind_agents
    • First observedget_agent_code
    • First observedhow_to_run_agent
    • First observedrequest_service
    • First observeduse_agent_here

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