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Glama

jikan

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only, non-destructive, idempotent. Description adds substantial behavioral detail: BGE-base-en embeddings, 500-char overlapping windows, 200K char cap with truncation flagging, and return offsets/similarity scores. No contradiction.

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?

Two dense but purposeful sentences: first defines the operation, second gives usage guidance, third covers technical specifics. Every sentence earns its place with zero fluff.

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?

No output schema, but description covers inputs, return shape (top-N passages with offsets and scores), truncation behavior, and use-case context. Fully complete for an agent to select and invoke confidently.

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 coverage is 100%, so baseline 3. Description adds contextual meaning beyond the schema by framing 'text' as already-fetched record content (SEC 10-K, article) and providing query examples, plus confirming the limit default. This extra context raises it a point.

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?

Description clearly states a specific verb+resource: semantic search inside a fetched record. It distinguishes from siblings by emphasizing 'inside' an already-pulled text and by contrasting with grounding over the whole document.

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?

Explicitly says when to use ('too big to cram into the prompt') and names an alternative pattern ('Pairs with ask_pipeworx_grounded'). Provides context-saving rationale and exclusion from whole-document grounding.

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

A3.6/5.0
Disambiguation2/5

Many tools serve overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, compare_entities, entity_profile, recent_changes, validate_claim) all querying Pipeworx data with similar outcomes. An agent would struggle to choose correctly without deep understanding of nuanced differences.

Naming Consistency2/5

Naming conventions are mixed: snake_case (ask_pipeworx, get_anime), camelCase (generate_llms_txt, pipeworx_feedback), and compound names (polymarket_arbitrage, ai_visibility_check). No consistent pattern across the tool set.

Tool Count2/5

34 tools is excessive for a single server. Many are meta-tools (discover_tools, suggest_questions) or narrowly focused (pipeworx_trending, scan_dependency). The server tries to cover too many domains (anime, financial data, predictions, memory) in one surface.

Completeness3/5

The anime tools (search, get, top) form a reasonable read-only surface. The Pipeworx query tools are comprehensive but lack obvious data management tools (e.g., listing sources, managing credentials). Memory tools (remember/recall/forget) are isolated. Overall, gaps exist but core workflows are covered.

Resources