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Glama

Unicode

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 mark it as read-only, idempotent, and non-destructive. Description adds substantial behavioral traits: embedding model (BGE-base-en), similarity method (cosine), window size (500-char overlapping), character cap (200K with truncation flag), and return details (offsets, scores). No contradictions.

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?

Three tightly packed sentences: core function, when to use, pairing advice, and technical details. No filler; every sentence earns its place. Information is front-loaded.

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 no output schema, description clearly states return type (passages with offsets and scores) and caps. Paired with strong annotations, the tool is fully described for safe, 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?

Input schema covers all 3 parameters with complete descriptions (100% coverage). Description repeats schema info or adds minimal extras (e.g., example queries) but does not meaningfully enhance understanding beyond the schema.

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?

Description uses specific verb 'search' and resource 'record', clearly stating it operates inside a fetched record. It distinguishes the tool's niche (handling oversized records) and mentions a sibling tool (ask_pipeworx_grounded), providing strong differentiation.

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?

Explicit usage context: 'Use when the record is too big to cram into the prompt.' Includes when-to-use, what it saves (context), and pairs with another tool for grounding. No ambiguity.

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 overlap significantly: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose, with the beta variant currently identical to the stable one, and deep_research also overlaps for broad research. However, most other tools have clearly distinct functions, and descriptions provide usage guidance, so ambiguity is moderate.

Naming Consistency3/5

Most names are snake_case and readable, but patterns vary between verb_noun (compare_entities, resolve_entity), noun phrases (entity_profile, polymarket_edges), and bare verbs (remember, subscribe). The pipeworx_ and polymarket_ prefixes are used inconsistently across the set, and the server name 'Unicode' does not align with the tool names.

Tool Count2/5

At 34 tools, the server is overloaded for a focused purpose, especially since only three tools relate to Unicode despite the server name. Many Polymarket and Pipeworx tools could be consolidated, and the count exceeds the well-scoped range of 3-15 tools, making the set feel bloated and unfocused.

Completeness2/5

The server name claims to be about Unicode, but only char_info, escape_string, and unescape_string cover that domain, missing common operations like normalization, case conversion, and encoding validation. For the broader data-access domain the surface is fairly complete, yet there is no tool to fetch a specific pipeworx:// citation URI, leaving cited records unfetchable within the tool set—an obvious gap relative to the stated capabilities.

Resources