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Glama

Skiddle

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description adds technical details beyond annotations: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged)' and explains that passages carry offsets for verification. Annotations already indicate read-only, idempotent, open-world, so 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?

The description is four sentences, front-loaded with purpose, and every sentence adds essential information without redundancy. It is concise and well-structured.

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 no output schema, the description covers what is needed: purpose, usage, technical details (embedding, window, cap, offsets), and pairing with sibling tool. It is complete for a tool of this complexity.

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% with good description of each parameter. The description adds value by reiterating the cap and truncation for 'text' and providing example queries for 'query'. While not adding entirely new semantics, it reinforces and clarifies, especially the max length and truncation behavior.

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 inside a fetched record, with specific examples (SEC 10-K, article) and explicitly distinguishes it from siblings by noting when to use it (record too large for prompt) and pairing with ask_pipeworx_grounded.

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?

The description provides explicit guidance on when to use the tool ('Use when the record is too big to cram into the prompt') and mentions a sibling tool (ask_pipeworx_grounded) for grounding, giving clear context for alternatives.

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

A3.9/5.0
Disambiguation3/5

Most tools have clearly distinct roles, but the ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily as question-answering and research entry points, with ask_pipeworx_beta currently being an exact duplicate of ask_pipeworx. The prediction-market tools are individually differentiated but numerous enough that selecting the right one requires careful reading.

Naming Consistency4/5

The set is predominantly snake_case and mostly readable, with sensible prefixes like ask_, search_, polymarket_, and scan_. However, conventions mix verb-first names (remember, subscribe, validate_claim), noun-style names (categories, event, entity_profile), and adjective-noun names (recent_alerts, recent_changes), so the pattern is not fully uniform.

Tool Count2/5

34 tools is well above the 25-tool threshold where a server starts to feel heavy, and many could be consolidated (7+ prediction-market tools, 4+ overlapping ask/research tools, plus memory and subscription helpers). The breadth is somewhat justified by the Pipeworx data platform, but the surface is still overloaded for a single server.

Completeness4/5

For the broad data/research domain, coverage is strong: lookup, grounded verification, deep research, entity profiles, comparisons, change feeds, tool discovery, memory, and subscription lifecycle all have coherent coverage. Minor gaps exist, such as no explicit tool to read a pipeworx:// citation URI directly and a fairly thin Skiddle events side beyond search/detail/categories.

Resources