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Glama

musicurainz

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 readOnly/openWorld/idempotent/non-destructive, so the bar is lower. The description adds substantial behavioral context: returns character offsets and similarity scores, uses BGE-base-en embeddings with cosine over overlapping windows, enforces a 200K-char cap, and flags truncation. These details go beyond the annotations without 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?

The description is front-loaded with the core purpose in the first sentence, then adds usage context, workflow, and technical constraints in subsequent sentences. No redundancy or filler; every sentence contributes value.

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 the absence of an output schema, the description compensates by specifying return format (passages with offsets and scores), input constraints (200K cap, truncation flag), and integration with a sibling tool. It is complete for an agent to decide when to use it and what to expect.

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 is 3. The description enhances semantics with concrete examples of the 'text' parameter ('SEC 10-K body, an article, a long tool result') and query styles ('supply-chain risk', etc.), clarifying that 'text' is previously fetched content. This adds practical meaning beyond the schema descriptions.

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 states 'Semantic search INSIDE a fetched record' with a specific verb and scope, and details the output: 'top-N passages with character offsets and similarity scores'. It clearly distinguishes itself from siblings by naming ask_pipeworx_grounded as a complementary tool and positioning itself as the retrieval step, not the answer generator.

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 states 'Use when the record is too big to cram into the prompt' and explains the benefit (saves context, returns only relevant passages). It also names a direct alternative, ask_pipeworx_grounded, and outlines the workflow: fetch with the gateway, then ground over passages. This is clear when-to-use guidance with an explicit alternative.

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

A3.8/5.0
Disambiguation3/5

Many tools are crisply separated (memory CRUD, subscription lifecycle, single-entity vs compare vs profile), but several broad entry points overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very close variants, and discover_tools/suggest_questions/ask_pipeworx all serve discovery/routing. Descriptions help, but an agent can still easily select one of the duplicate or adjacent tools instead of the intended one.

Naming Consistency3/5

All names are readable snake_case and there are coherent families (polymarket_*, ask_pipeworx_*, search_*, get_*), but there is no consistent verb_noun convention: noun-phrase names like recent_alerts and pipeworx_trending coexist with single verbs like remember and forget and domain-prefixed nouns like polymarket_edges. Mixed, but still reasonably navigable.

Tool Count2/5

At 35 tools, the surface is well past the comfortable 3-15 tool scope and even beyond the 16-25 heavy range unless the server has one explicit mega-purpose. The set also sprawls across music lookup, Pipeworx research, prediction markets, npm checks, LLM visibility, memory, and subscriptions, so no single coherent job emerges.

Completeness4/5

For the dominant read-only research workflow, the set is remarkably complete: discover tools, grounded and ungrounded asking, deep research, entity resolution, profiles, recent changes, comparisons, claim verification, search_within, plus full memory and subscription lifecycles. Minor gaps include the shallow music side relative to the rest of the server and the absence of an explicit source catalog.

Resources