Skip to main content
Glama
dhamma-seeker

tripitaka-mcp

by dhamma-seeker
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Hybrid Search

search_hybrid
Read-onlyIdempotent

Retrieve suttas about a concept even when different vocabulary is used. Combines keyword and semantic search to find relevant discourses, ideal for topic exploration.

Instructions

Hybrid search — combines keyword + semantic search via RRF.

Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. This is the recommended tool for "discourses about X" / concept queries, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use assasati/passasati/dīghaṁ instead of ānāpānassati).

💡 Hints for the AI client:

  • English queries usually work best (e.g. mindfulness of breathing) because the embedding model is multilingual but EN-primary.

  • Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions).

  • The default limit=5 is often too small for a topic survey — use limit=15-20 (max 20) for good coverage.

  • Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call get_sutta for the canonical references.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesQuery text (Thai, Pāli, or English — English works best).
languageNoOutput language — "pali", "thai", "english", or "all".pali
limitNoMaximum results (default: 5, max: 20).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly and non-destructive. Description adds valuable behavioral context: RRF fusion, ranking by similarity not canonical importance, English query performance, Thai stop-word limitations. No contradictions with annotations.

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?

Well-structured with clear sections, bold and emoji highlights. Every sentence adds value, no fluff. Front-loaded with main purpose.

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 tool complexity (hybrid search with RRF), the description covers behavior, usage tips, limitations, and output interpretation. Output schema exists but not shown; description still addresses result ranking and canonical importance. Very complete.

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?

Schema covers all parameters with descriptions. Description adds practical advice: query should be English for best results, limit defaults too small for surveys. Adds meaning beyond 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 explicitly states the tool's function: 'Hybrid search — combines keyword + semantic search via RRF.' It distinguishes from sibling tools like search_by_keyword and search_semantic by being the recommended tool for concept queries, with concrete examples.

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?

Clear guidance on when to use this tool (concept queries) and hints for handling underperforming queries (Thai→Pāli/English translation). Lacks explicit 'when not to use', but the context implies alternatives (e.g., search_by_keyword for exact matches).

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/dhamma-seeker/tripitaka-mcp'

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