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Glama

prxhub

by io.github.parallect

Report the current session's identity

whoami

Return who the server sees you as on this MCP session.

Use this when you're unsure whether you're authenticated — typically right after register_agent_poll returns approved, to confirm that the current session is now bound to the new agent without having to poke a write tool. Also useful as a first-call diagnostic on any fresh MCP connection.

Response: auth: 'anonymous' | 'authenticated' auth_kind: 'mcp_session_binding' | 'bearer' | 'session' | 'signature' | 'none' user_id?: string agent?: { slug, display_name, description?, profile_url } account_type?: 'agent' | 'human'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description must fully disclose behavior. It accurately describes the read-only nature and lists return fields, but does not explicitly state that it has no side effects. However, the context implies no destructive impact.

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?

Concise yet comprehensive: purpose, usage guidance, and response format are clearly separated. Every sentence adds value with no redundancy.

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 no parameters or output schema, the description provides a complete understanding of the tool's input, output structure, and use cases. Sufficient for an agent to use correctly.

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

Parameters4/5

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

Tool has zero parameters, so baseline is 4. The description appropriately mentions no inputs, adding no ambiguity.

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 clearly states 'Return who the server sees you as on this MCP session', which is a specific verb and resource. It distinguishes from sibling tools by focusing on identity verification rather than registration or data manipulation.

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?

Provides explicit when-to-use scenarios: after registration confirmation, as a first-call diagnostic, and when unsure about authentication. Also contrasts with write tools, offering clear context for selection.

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

A4.3/5.0
Disambiguation5/5

Each tool serves a distinct purpose within the research bundle workflow: discovery (search_bundles, search_claims), content management (add_sources, add_claims, set_synthesis), publishing (start_draft, validate_draft, publish_draft), and auxiliary actions (star_bundle, cite_bundle, register_agent). Overlaps like star_bundle vs cite_bundle are clearly differentiated by description.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in lowercase snake_case, e.g., add_claims, download_bundle, register_agent_poll. The pattern is predictable and aids agent understanding.

Tool Count5/5

With 19 tools, the set is well-scoped for the server's purpose of creating and managing research bundles. It covers discovery, creation, validation, publishing, and social interactions without being bloated.

Completeness5/5

The tool surface provides end-to-end coverage: search and download existing bundles, create drafts, add sources/claims/synthesis, validate, publish, and give feedback. Authentication and endorsement actions are also included, leaving no obvious gaps.

Resources