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Datos Ar

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral details: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char limit with truncation flag. No contradiction 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?

Two concise paragraphs, front-loaded with key information. Every sentence adds value, no wasted words.

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 full schema coverage, good annotations, and no output schema, the description thoroughly explains return values (passages with offsets and scores), embedding model, windowing, truncation, and pairing with sibling. Complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. The description adds useful context for 'text' (max length) and 'query' (example queries), but does not add meaning beyond schema for 'limit'. Value added but not substantial.

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. It specifies the verb ('search_within'), the resource (a fetched record's text), and distinguishes from siblings by explaining it is for records too large for the prompt and pairs 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?

Explicitly tells when to use ('when the record is too big to cram into the prompt') and provides an alternative usage pattern with ask_pipeworx_grounded, giving clear guidance on when not to use this tool.

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

Several tools cluster around the same underlying data router (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the prediction-market family has five overlapping members, so misselection is possible. The descriptions are detailed enough to separate most intents, but ask_pipeworx_beta is explicitly identical to ask_pipeworx right now and ai_visibility_check/scan_competitor_ai_presence are close cousins.

Naming Consistency4/5

All tool names consistently use lowercase snake_case, and most follow a clear verb_noun shape like ask_pipeworx, get_series, subscribe, or validate_claim. A few noun-style names (entity_profile, pipeworx_trending, polymarket_edges) and the bare memory verbs (remember, recall, forget) break the pattern slightly, but the overall convention is predictable.

Tool Count2/5

33 tools is a heavy surface that exceeds the 25-tool threshold where selection cost becomes a real problem for agents. The count is inflated by auxiliary concerns like memory, subscriptions, feedback, trending, and AI-presence scans that sit alongside the core data-access mission.

Completeness4/5

The core data-research workflows are well covered: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, deep_research, get_series), entity resolution (resolve_entity), profiles, comparisons, claim validation, and prediction-market analysis all have end-to-end support. Memory and subscription lifecycles are also complete. Minor gaps exist — some sources soft-fail and there is little Argentina-specific tooling beyond the time-series pair — but agents can generally work around them.

Resources