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Worms

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flagging, and offsets for verification. 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?

Four sentences, front-loaded with purpose, then usage, then technical details. Every sentence adds value with no redundancy 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?

Although there is no output schema, description fully explains output: top-N passages with character offsets and similarity scores. Also covers input limits, embedding details, and windowing. No gaps for an agent to use the tool correctly.

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?

Schema coverage is 100%, so baseline is 3. Description adds natural-language query examples (e.g., 'supply-chain risk') and reinforces the 200K char limit for 'text,' providing practical guidance 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?

Clearly states 'semantic search INSIDE a fetched record,' specifying the verb (search) and resource (text of a fetched record). Distinguishes from siblings by mentioning it pairs with ask_pipeworx_grounded and is used when the record is too large for the prompt.

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 provides when to use: 'Use when the record is too big to cram into the prompt.' Mentions alternative 'ask_pipeworx_grounded' and explains how they complement each other. Gives clear context and exclusions.

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

A3.6/5.0
Disambiguation2/5

Several tools have functionally overlapping purposes, most notably ask_pipeworx and ask_pipeworx_beta, which currently behave identically. The many prediction-market tools are each distinct but still leave boundaries that require careful reading, and the query/research tools (ask_pipeworx, deep_research, potentially validate_claim) have an ease of being confused. Fine verbal descriptions reduce but do not eliminate the ambiguity for an agent.

Naming Consistency3/5

All names are lowercase snake_case and many follow a verb_noun pattern, such as resolve_entity, get_classification, and list_subscriptions. However, the set is inconsistent overall: it mixes single verbs (remember, forget, subscribe), noun-style names (entity_profile, recent_alerts), and several prefixed families (pipeworx_*, polymarket_*). It's readable but not a coherent, uniformly applied convention throughout.

Tool Count2/5

34 tools in one server is well beyond the generally well-scoped 3–15 range. The server appears to bundle several unrelated domains together—WoRMS taxonomy, Pipeworx data research, Polymarket analysis, memory utilities, and subscriptions—creating avoidable cognitive load and making selection more difficult. Splitting into targeted servers would greatly improve the interface.

Completeness2/5

The server is named 'Worms' and includes a WoRMS taxonomy subdomain, but that subdomain only supports searching, classification, and common names—the rest of the marine taxonomy surface is missing (e.g., direct AphiaID record lookup, distributions, synonyms, hierarchical children). The remainder of the tools serve an entirely different data-research purpose, so the server is incomplete relative to its name AND the bundled extra domains add confusion rather than a coherent cohesive coverage.

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