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Glama

prxhub

by io.github.parallect

Search prxhub bundles

search_bundles

Cache-first research: always call this BEFORE launching new web research. Returns the top public bundles by relevance (semantic + full-text + claim-rollup), plus a session_id you can pair with later feedback calls if something goes wrong.

Recommended flow when results come back:

  1. Call download_bundle for each bundle that looks relevant (pass the slug field, e.g. 'harness-test/grid-parity-2035').

  2. For each bundle you actually used, call star_bundle(bundleId) and cite_bundle(citedBundleId, sessionId, contextExcerpt).

  3. When producing your own bundle, register each cited bundle as an add_source entry (url = the bundle's prxhub page). The viewer renders them as an 'Inherits from' panel.

  4. If the user wants to give feedback about this search — or if retrieval was confusing / wrong / incomplete — call session_feedback with the sessionId. Skip if everything went smoothly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-10). Default 10.
queryYesSearch query string
collectionNoScope the search to a single collection. Format: '<owner>/<slug>' — e.g. 'alex-rivera/ai-safety-2026'. Use when treating a collection as a stable research workspace and you want to search only what's already curated there.

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the cache-first behavior, the return of session_id for feedback, and the relevance scoring method. It does not explicitly state it is read-only or non-destructive, but the context implies no side effects. Additional detail on return format or pagination could improve transparency.

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

Conciseness3/5

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

The description is lengthy and includes a detailed workflow that, while valuable, goes beyond describing the tool itself. The first sentence is concise, but the subsequent paragraphs could be more succinct. Some redundancy exists (e.g., repeated mention of session_feedback).

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?

Given the tool has 3 parameters, no output schema, and no annotations, the description is quite complete. It explains the cache-first approach, the return of session_id, and the workflow for post-processing. It does not detail the output structure beyond mentioning bundles and session_id, but that is acceptable since there is no output schema.

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 explains parameters (query, limit, collection). The description adds context about using collection for scoping but does not provide extra semantics beyond the schema. Baseline 3 is appropriate as the description adds no parameter-specific insights.

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 the tool's purpose: 'Cache-first research: always call this BEFORE launching new web research. Returns the top public bundles by relevance.' It distinguishes itself from sibling tools like search_claims by specifying it searches bundles, and the recommended flow differentiates it from download_bundle and citation tools.

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 provides explicit when-to-use guidance ('always call this BEFORE launching new web research') and a detailed recommended flow after results, including specific steps to call download_bundle, star_bundle, cite_bundle, and session_feedback. This gives clear context for when to use this tool versus alternatives.

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