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Glama

ToolRouter

by io.github.Humanleap

Request a Tool

feedback_request_tool

Ask ToolRouter to build a tool that does not exist yet. Use this when discover returned nothing useful for what the user needs. Describe the capability in plain terms — do not include personal data, API keys, or secrets. Requests go straight to the team. Limit: 5 per hour.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyNoWhat the user was trying to accomplish and why existing tools did not fit.
whatYesWhat the tool should do, in one or two sentences (e.g. "Look up UK company filings by company number").
searchedNoThe discover query you tried that came back empty.
agent_contextNoMCP client name and version, if known.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations are all false and do not reveal much, but the description adds meaningful behavioral context: requests are not fulfilled immediately but 'go straight to the team,' there is a 5-per-hour limit, and sensitive data must not be included. This goes beyond the structured annotations and helps the agent set expectations about the tool's real-world effect.

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?

The description is only five short sentences, and each one earns its place: purpose, usage condition, content guidance, routing behavior, and rate limit. The most important information is front-loaded, and there is no filler or repetition of schema details.

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

Completeness5/5

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

For a tool with four simple string parameters, no output schema, and no nested objects, the description covers all operational essentials: when to invoke it, how to fill it out, what not to include, what happens after submission, and how often it can be used. An agent can invoke this tool correctly without additional context.

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 each of the four parameters is already clearly documented in the input schema. The tool description adds general guidance about writing capabilities in plain terms and omitting secrets, which applies to the parameters as a whole, but it does not add parameter-specific meaning beyond what the schema already provides.

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?

The description states a specific verb and resource: 'Ask ToolRouter to build a tool that does not exist yet.' This clearly distinguishes it from discover, use_tool, and the other sibling tools that operate on existing tools or data. It also adds a triggering condition (discover returned nothing useful) that anchors the tool's role.

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?

The description explicitly tells the agent when to use this tool: 'Use this when discover returned nothing useful for what the user needs.' This names the alternative or prior step, gives a clear condition, and additionally supplies content constraints and a rate limit, leaving little ambiguity about when it is appropriate.

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

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TDQS

B3.4/5.0
Disambiguation4/5

Tools are grouped by clear resource prefixes (account_, brain_, connector_, credential_, file_, job_, key_), and most actions have distinct purposes. A few boundaries overlap—brain_admin's lint action duplicates brain_lint, and account_preferences/setup/switch could momentarily confuse—but the descriptions resolve most ambiguity.

Naming Consistency3/5

The dominant pattern is resource_verb for actions (file_read, job_cancel, key_create) and resource_noun for state views (credits_balance, brain_settings, account_preferences), which is readable. However, exceptions like discover, use_tool, top_up_credits, and feedback_request_tool break the pattern, and the set is not consistently verb_noun.

Tool Count2/5

47 tools is well beyond the comfortable range; even though prefixes organize them, the agent faces a large selection surface with many narrowly scoped tools. A more consolidated set with action-based subcommands would be easier to navigate.

Completeness4/5

Core workflows are covered end-to-end: account setup and billing, connector and credential management, file CRUD, job polling, key lifecycle, brain knowledge management, and catalogue discovery/execution. Gaps are minor—outfit/persona/product/scene are list-only, connectors lack an update operation, and there is no explicit single-page brain get—but agents can generally work around them.

Resources