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Glama

Cms

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

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds embedding model details (BGE-base-en, cosine, 500-char windows), 200K char cap with truncation flag, and offset for verification. Adds significant value beyond annotations.

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?

Description is front-loaded with the core action and is reasonably concise. Slightly lengthy but every sentence adds value. Well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers use case, return type (passages with offsets and scores), truncation behavior, and pairing with sibling. No output schema, but description explains return values adequately. Minor gap: could clarify passage text format.

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% with descriptions for all three parameters. Description adds some context (e.g., examples for query, truncation for text) but not much beyond what schema provides. Baseline 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 clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article, long tool result). It distinguishes itself from sibling tools that fetch data or do other operations.

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?

Explicitly advises use when the record is too large for the prompt and pairs with ask_pipeworx_grounded. Does not explicitly state when not to use, but context is clear.

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

A4/5.0
Disambiguation3/5

Some tools have clearly distinct purposes (remember/recall/forget, subscribe/unsubscribe), but the ask_pipeworx family overlaps heavily — ask_pipeworx_beta is explicitly identical today, and ask_pipeworx_grounded/deep_research are variations on the same routing core. Polymarket tools and comparison/profile tools also have fuzzy boundaries, though detailed descriptions help agents choose.

Naming Consistency4/5

Most tools follow a lowercase snake_case verb_noun pattern (search_datasets, get_dataset, validate_claim, resolve_entity). A few deviate with bare verbs (remember, forget, recall) or noun-like names (dataset_info, entity_profile, pipeworx_trending), but the overall style is predictable and readable.

Tool Count2/5

34 tools is a heavy surface for one server, and the scope sprawls across CMS open data, general data research, prediction markets, memory storage, and subscription management. Many of these could be split into separate coherent servers, and several meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) overlap in purpose.

Completeness4/5

Within the broad data-research domain the set is fairly complete: search, retrieval, grounding, comparison, entity resolution, verification, subscriptions, and memory are all covered with no obvious dead ends. However, the server is named 'Cms' yet only three tools actually touch CMS datasets, leaving that narrow purpose under-covered relative to the rest.

Resources