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Glama

Vat

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral details: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation and flagging. No contradiction with annotations.

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?

The description is concise, front-loaded with key information, and every sentence adds value. No unnecessary words or repetition.

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, the description explains return type (passages with character offsets and similarity scores). It also mentions pairing with another tool and technical details (embeddings, cap). Complete for the tool's complexity.

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 parameters are documented. The description adds extra semantic context: max 200K chars for 'text', default and range for 'limit', and example queries for 'query'. This goes 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?

The description clearly states it performs semantic search inside a fetched record, using specific verbs like 'search' and 'get back'. It distinguishes from sibling tools (e.g., ask_pipeworx_grounded) by noting that it works inside a record rather than over a whole document.

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?

The description explicitly says 'Use when the record is too big to cram into the prompt' and suggests pairing with ask_pipeworx_grounded. It does not explicitly list when not to use, but the clear use case and context are sufficient for guidance.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying catalog, and ask_pipeworx_beta is currently identical to ask_pipeworx. ai_visibility_check and scan_competitor_ai_presence also overlap, and entity_profile vs recent_changes cover similar ground. The descriptions are detailed, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (validate_vat, list_vat_formats, resolve_entity), some are noun-first (entity_profile, polymarket_edges, recent_alerts), and some are product-branded (ask_pipeworx, pipeworx_feedback). It's readable but does not follow a single predictable verb_noun convention.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent set, especially for a server named 'Vat' where only two tools (list_vat_formats, validate_vat) relate to the apparent purpose. The bulk forms a broad data-research platform that would be more appropriately split into separate VAT and Pipeworx servers.

Completeness4/5

For the actual data-research domain, coverage is strong: discovery, single lookup, grounded answers, deep multi-source research, entity resolution, comparisons, claim verification, memory, subscriptions, and prediction-market tooling are all present. The only clear gap is that the VAT-specific surface is minimal (format validation only, no registration/VIES check), but the overall functional surface is otherwise quite complete.

Resources