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Glama

Obis

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

A5/5.0
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent), the description reveals the embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), input cap (200K chars), truncation behavior, and output details (offsets, similarity scores), contradicting nothing.

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?

A single well-structured paragraph of ~110 words, front-loading purpose, then usage, then technical details. Every sentence contributes essential information with no redundancy.

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 no output schema, the description explains output format (top-N passages with offsets and similarity scores), input limits, and truncation behavior, fully covering information needs for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description enriches parameters: adds max 200K chars to 'text', provides query examples ('supply-chain risk', 'drug interactions'), and specifies limit range (1-20, default 5), all beyond schema descriptions.

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 clear verbs ('Semantic search INSIDE a fetched record') and specific resource examples (SEC 10-K, article, long tool result), differentiating it from siblings like ask_pipeworx_grounded by specifying it operates on already-fetched text.

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 ('record too big to cram into the prompt'), why it's beneficial ('saves context, returns only passages that matter'), and recommends pairing with a sibling (ask_pipeworx_grounded), providing clear alternatives.

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

Most tools have distinct purposes and the descriptions are unusually thorough, but ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and several discovery/prediction-market tools (polymarket_edges vs polymarket_arbitrage, discover_tools vs suggest_questions) occupy overlapping territory. An agent could reasonably route to the wrong variant despite the documentation.

Naming Consistency4/5

The dominant convention is lowercase snake_case with a leading verb (ask_pipeworx, list_subscriptions, validate_claim, resolve_entity), which makes the set predictable. A few noun-first outliers like pipeworx_trending, recent_alerts, and polymarket_edge_tracker are minor deviations rather than a broken pattern.

Tool Count2/5

34 tools is well above the coherence sweet spot for an MCP server, and much of that count is made up of meta-wrappers and convenience variants around the same Pipeworx router. The broad scope explains some of the count, but the surface still feels heavy for a single named server.

Completeness3/5

The Pipeworx side is very complete: lookups, grounded verification, research, entity profiles, comparisons, prediction-market fill checks, subscriptions, and memory all have lifecycle coverage. However, the server is named Obis and only find_occurrences, get_statistics, and get_taxon serve that domain, leaving obvious marine-biodiversity operations like occurrence detail, dataset listing, and download paths missing.

Resources