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sunrisesunset

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

A4.4/5.0
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description details the algorithm (BGE-base-en embeddings, cosine over 500-char windows), the 200K char cap with truncation flagging, and the presence of offsets for verification. This gives the agent rich behavioral insight beyond what annotations convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each substantive: function, use case, integration, and technical details. It is front-loaded with the core purpose and wastes no words, though it is slightly denser than the two-sentence ideal.

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?

The tool has no output schema, but the description adequately explains return values (passages with offsets and similarity scores), behavior on long inputs (truncation flagged), and how it fits with the sibling tool ask_pipeworx_grounded. With complete schema coverage and annotations, nothing essential is missing.

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 the baseline is 3. The description essentially echoes the schema ('text you already pulled', 'natural-language query') without adding new parameter meaning. It does clarify the return format, but that's not parameter-specific.

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 opens with 'Semantic search INSIDE a fetched record' – a specific verb, resource, and scope. It clearly distinguishes itself from siblings by noting it returns 'top-N passages with character offsets and similarity scores' and explicitly mentions pairing with ask_pipeworx_grounded, so an agent can differentiate it from other search/grounding tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides a clear usage condition: 'Use when the record is too big to cram into the prompt' and names an alternative: 'Pairs with ask_pipeworx_grounded'. However, it doesn't explicitly state when-not to use it (e.g., for small records), which would have made it a 5.

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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but there is overlap among closely related Polymarket tools (e.g., bet_research, polymarket_edges, polymarket_arbitrage) and between ask_pipeworx and ask_pipeworx_grounded, which could cause misselection.

Naming Consistency3/5

Tool names mix verb_noun, noun_verb, and descriptive phrases inconsistently (e.g., get_times vs listen_subscriptions vs bet_research), and while many use underscores, conventions vary, making patterns hard to predict.

Tool Count3/5

With 32 tools, the server feels overloaded, covering distinct domains (sunrise, Pipeworx, Polymarket, memory, subscriptions) under one name, making it a 'Swiss army knife' rather than a focused toolset.

Completeness2/5

The server name suggests a narrow domain (sunrise/sunset) but only 2 of 32 tools address it, ignoring related weather data. While other domains are covered, operations lack depth (e.g., no subscription updates, no memory search).

Resources