Skip to main content
Glama
rdnpradipta

Muninn

by rdnpradipta

search

Retrieve relevant text chunks from a research knowledge base using natural language queries. Each result includes exact page-level citations from PDF documents.

Instructions

Semantic search over the corpus. Returns chunks with page-level citations.

Args: query: natural-language question or topic. top_k: number of results (default 6). doc_id: restrict to one document (from list_documents), "" = all. note_type: which payload types to search — "raw" (verbatim paper text, ground truth), "claude_note" (Claude's reading notes), "hyde" (generated HyDE bridges only), "all" (raw + claude_note, the default), or "everything" (raw + claude_note + hyde). hypothesis: HyDE fallback. When the plain query misses (low scores), pass a short hypothetical answer paragraph here. It is embedded and averaged with the query (Gao et al., 2023) to retrieve REAL corpus chunks via the hypothesis. Each hit is tagged retrieved_via="hyde". "" = ordinary query-vector search (retrieved_via="direct").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
doc_idNo
note_typeNoall
hypothesisNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations exist, so the description carries full burden. It transparently explains the HyDE fallback mechanism, how hypothesis is embedded and averaged, and how retrieved results are tagged. It also describes the note_type options. However, it does not mention any side effects, rate limits, or authorization needs.

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 front-loaded with the main purpose, followed by a structured Args list. While slightly verbose, each sentence carries meaningful information. It could be more concise without losing clarity.

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 5 parameters, complex HyDE behavior, and an existing output schema, the description covers search behavior comprehensively. It references sibling list_documents and explains return format. The only gap is not mentioning the output schema's actual structure, but it exists and is documented separately.

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%, yet the description adds rich meaning to all 5 parameters: query (natural-language question), top_k (number of results), doc_id (restrict to one document), note_type (exhaustive explanation of each value), hypothesis (HyDE fallback with details). This greatly exceeds what the schema 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 clearly states the tool performs 'semantic search over the corpus' and returns 'chunks with page-level citations'. It is a specific verb+resource combination that distinguishes it from sibling tools like list_documents (lists documents) and get_page (retrieves a specific page).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains parameters and references sibling list_documents for doc_id, but does not explicitly state when to use this tool versus alternatives like get_page or render_page. No when-not-to-use guidance is provided.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rdnpradipta/muninn'

If you have feedback or need assistance with the MCP directory API, please join our Discord server