prxhub

Register one or more sources on a draft

add_sources

Batch-friendly source registration. Pass a sources array of 1 or more source objects. Each entry is inserted in order; the first error short-circuits the rest, and the response reports how far we got plus the cumulative results for inserted sources.

ID format: pass source_id='src-1', 'src-2', ... (sequential, hyphenated, lowercase). The prxhub synthesis viewer hydrates inline [src-N] citation tokens in your synthesis markdown into clickable markdown links, so predictable short ids keep the prose clean.

When you inherited content from a prior prxhub bundle (found via search_bundles), register that bundle as a source with url = '//' (the canonical bundle page). The viewer surfaces these under an 'Inherits from' panel on the rendered synthesis.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sources`	Yes	One or more source descriptors. Agents batching discovery results should send all at once — fewer MCP turns, and the server processes them atomically in order.
`draft_id`	Yes

Schema Changelog

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

AddedApr 24, 2026

TDQS

A4.2/5.0

Behavior5/5

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

No annotations are provided, so the description carries full responsibility. It discloses batch behavior, error short-circuiting, ID format, viewer citation hydration, and how inherited bundles are surfaced. This is comprehensive and provides key behavioral insights beyond basic function.

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 well-structured but somewhat lengthy; however, every sentence adds value. It fronts the purpose with 'Batch-friendly source registration' and logically proceeds to details, making it efficient for an AI agent to parse.

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's complexity (nested sources array, multiple fields), no output schema, and zero annotations, the description adequately explains input structure, error reporting, and downstream effects. It covers essential aspects without leaving major gaps.

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?

The description adds meaning to the parameters beyond the schema. It explains the sources array structure, field behavior (source_id format, url for inherited bundles), and processing order. With schema coverage at 50%, this compensation is above baseline.

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 clearly states 'Register one or more sources on a draft' and opens with 'Batch-friendly source registration.' This uses a specific verb and resource, making the tool's function obvious. Although it doesn't explicitly distinguish from siblings like add_claims, the purpose is clearly communicated.

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?

The description explains when to use the tool (batch registration), how to structure input (array of source objects), and provides specific guidance on ID format and handling inherited bundles. It does not mention when not to use or alternatives, but the guidance is detailed enough to inform proper usage.

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

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