Skip to main content
Glama

Dostuff

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".

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, and destructiveHint. The description adds significant behavioral context: embedding model (BGE-base-en), windowing (500-char overlapping windows), truncation (200K char cap with flagging), and return characteristics (offsets and similarity scores). 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?

The description is three sentences, each earning its place: purpose, usage guidance, technical details. It is front-loaded and contains no filler. Every sentence adds 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?

Despite having no output schema, the description explains what is returned (top-N passages with character offsets and similarity scores). It covers truncation behavior, pairing with another tool, and embedding details. Given the tool's complexity, this is complete for selection and 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 description coverage is 100%, so baseline is 3. The description adds minimal extra parameter meaning beyond schema descriptions (e.g., examples for query, default for limit). The technical details about embeddings and windows are context, not parameter-specific. Thus a score of 3 is appropriate.

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 ('Semantic search INSIDE a fetched record') and clearly defines the resource and scope. It distinguishes from sibling tools by emphasizing that it operates on already-fetched records, contrasting with general search or grounding tools like 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 states when to use: 'Use when the record is too big to cram into the prompt — search_within saves context'. It also provides an alternative: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Dostuff toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

The set contains near-duplicate tools (ask_pipeworx_beta explicitly 'currently matches ask_pipeworx exactly') and a dense family of six polymarket_* tools whose boundaries are subtle, plus overlapping onboarding tools in discover_tools and suggest_questions. Detailed descriptions mitigate some confusion, but several tools are difficult to tell apart without reading their full text.

Naming Consistency3/5

All names are lowercase snake_case and readable, with consistent prefix families (ask_pipeworx, polymarket_, pipeworx_), but the overall structure is mixed: bare verbs (remember, forget, subscribe), adjective+noun names (recent_alerts, recent_changes), and noun+noun domain tags (polymarket_edges, entity_profile) rather than a uniform verb_noun pattern.

Tool Count2/5

At 33 tools the set exceeds the 25-tool threshold and feels heavy, carrying an experimental duplicate of ask_pipeworx and a six-tool polymarket family that could plausibly be consolidated. The breadth reflects several unrelated domains bundled into one server rather than a focused scope.

Completeness3/5

The data-research core (ask, grounded, deep_research, profiles, comparisons, claim validation, entity resolution) and the prediction-market analysis suite are thoroughly covered, and memory plus subscription lifecycles are complete. However, the events domain the server is named for is thin (only events + metros), and the overall set lacks a single coherent purpose against which completeness can be cleanly judged.

Resources