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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?

The description discloses behavioral traits beyond annotations: it details the internal mechanics (BGE-base-en embeddings, cosine over 500-char overlapping windows), the truncation cap at 200K chars with flagging, and the output structure (character offsets and similarity scores). Since annotations already cover read-only safety, this added depth is highly valuable.

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 three sentences, front-loaded with the core action, followed by usage guidance and technical details. Every sentence adds new information—purpose, value proposition, and caveats—without repetition or 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?

Despite lacking an output schema, the description fully prepares the agent by explaining the return format (passages with offsets and scores), the ideal use case, the truncation behavior, and the recommended workflow with a sibling tool. It covers all necessary operational details for a tool with 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?

The schema covers 100% of parameters, so the baseline is 3. The description adds meaningful context beyond the schema by giving concrete examples for 'text' (SEC 10-K body, article, long tool result) and noting that longer inputs are truncated and flagged. This adds practical semantics not in the schema, earning a 4.

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 uses a specific verb and resource: 'Semantic search INSIDE a fetched record' with the return of 'top-N passages with character offsets and similarity scores.' It clearly distinguishes from sibling tools like search_companies and explicitly connects to ask_pipeworx_grounded, making the tool's role unambiguous.

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?

It provides explicit usage context: 'Use when the record is too big to cram into the prompt' and describes a pairing with ask_pipeworx_grounded for a two-step workflow. It also explains the benefit (saves context, returns only relevant passages) without needing to formally list 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
Disambiguation2/5

The ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded trio are three variants of the same router — and the beta variant is explicitly stated to be identical to the stable one right now, making mis-selection nearly inevitable. The six polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have subtly overlapping boundaries where an agent could easily grab the wrong one.

Naming Consistency4/5

Nearly all tools use snake_case with a clear verb_noun or noun_compound shape (get_company_facts, resolve_entity, recent_changes, polymarket_edges). Minor deviations: the bare-verb memory trio (remember/recall/forget), the brand-style ask_pipeworx* naming, and a mix of verb-first vs. entity-first ordering, but the overall pattern is readable and predictable.

Tool Count3/5

34 tools is well above the typical well-scoped range, but the server's actual scope is enormous — a universal structured-data gateway, prediction-market suite, memory system, subscription system, and utility tools. However, the count feels inflated by genuine redundancy: ask_pipeworx_beta currently duplicates ask_pipeworx, and the polymarket cluster could plausibly be consolidated into fewer tools.

Completeness4/5

Each sub-domain has strong lifecycle coverage: entity resolution (resolve_entity, search_companies), company analysis (get_company_facts/filings, entity_profile, recent_changes, compare_entities), full CRUD for both memory and subscriptions, and an exhaustively covered prediction-market domain (research, edges, arb, fill risk, tracking, cross-venue). Minor gaps exist — there's no direct single-filing document fetch tool (search_within implies fetching via the gateway but no explicit getter), and the AI-visibility tools lack historical tracking — but these are workaround-able rather than blocking.

Resources