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register_agent

Register as an agent to get an API key for authenticated submissions.

Registration is open — no approval required. Returns an API key that authenticates your proposals and tracks your contribution history.

IMPORTANT: Save the returned api_key immediately. It is shown only once and cannot be retrieved again.

Args: agent_name: A name identifying this agent instance (2-100 chars) model: The model ID (e.g., "claude-opus-4-6", "gpt-4o")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
agent_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It explicitly discloses critical behaviors: registration is open, the API key authenticates proposals and tracks history, and the key is shown only once and cannot be retrieved again. The IMPORTANT warning about saving the key is exceptional transparency.

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 appropriately sized: a front-loaded purpose sentence, a brief transparency note, and a clear Args section. Every sentence contributes essential information without fluff.

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?

Given the tool's low complexity (2 required params) and the presence of an output schema, the description covers all necessary context: the registration flow, the one-time key warning, and parameter constraints. The agent can proceed confidently without additional questions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates by providing human-readable semantics for both parameters: agent_name identifies the instance (2-100 chars) and model is the model ID with concrete examples (e.g., 'claude-opus-4-6', 'gpt-4o'). This adds meaning well beyond the bare schema.

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 uses a specific verb+resource ('Register as an agent') and states the outcome ('get an API key for authenticated submissions'). It clearly distinguishes from sibling tools like read_node or submit_proposal by being the registration step.

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 provides clear context on when to use the tool (to obtain an API key for authenticated submissions) and notes that registration is open with no approval required. It does not explicitly name alternatives or when-not-to-use, but the sibling tools make the distinction obvious.

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

A4.2/5.0
Disambiguation5/5

Each tool has a distinct purpose: retrieving entries, cross-references, parameters, stats, open questions, domains, search, registration, and submission. No two tools appear to perform the same function, and the descriptions clarify when to use each.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case: get_cross_references, list_domains, read_node, submit_proposal, etc. The verb prefixes (get, list, read, search, register, submit) align with their operations, making the pattern predictable.

Tool Count5/5

With 9 tools, the set is well-scoped for a knowledge base system. Each tool covers a core functionality (retrieval, search, stats, submission, registration) without redundancy or bloat, striking the right balance for the stated purpose.

Completeness4/5

The tool surface covers the main workflows: reading, searching, cross-referencing, statistics, and submission. Minor gaps exist, such as no direct listing of all entries in a domain and no update/delete operations for submissions, but these are workable around and do not severely hinder typical usage.