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Glama

Virustotal

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Highly transparent: describes use of BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, a 200K char cap with truncation and flagging. These details go far beyond the annotations (which only mark readOnly, openWorld, idempotent, non-destructive). 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?

The description is a single well-structured paragraph: starting with purpose, then usage, then technical details. Every sentence contributes distinct 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?

For a tool with no output schema and moderate complexity, the description covers all bases: when to use, how text is chunked and embedded, limits, pairing with siblings, and what the return includes (passages with offsets and scores). No gaps.

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. The description enriches parameters by clarifying the max length for 'text' (~200K chars), providing natural-language query examples for 'query', and noting default for 'limit' (5). This adds moderate value 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 starts with 'Semantic search INSIDE a fetched record' and gives concrete examples like SEC 10-K body, article, long tool result. It explicitly states the use case: 'Use when the record is too big to cram into the prompt.' This clearly distinguishes it from sibling tools such as ask_pipeworx_grounded.

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 explains when to use search_within (when text is too large for the prompt) and how it pairs with ask_pipeworx_grounded for grounded reasoning. It provides a clear usage pattern but does not explicitly list when not to use other siblings, though the context is sufficient.

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

The set mixes several overlapping families: ask_pipeworx and ask_pipeworx_beta are described as currently identical, while ask_pipeworx_grounded, deep_research, bet_research, and validate_claim all route to the same underlying data sources. Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, spread) also blur together; only the four lookup_* VirusTotal functions are cleanly distinct.

Naming Consistency3/5

Names are uniformly snake_case with a few consistent families (lookup_domain/file/ip/url, ask_pipeworx_*, polymarket_*), which helps. However, the set mixes imperative verbs (remember, forget, subscribe, validate_claim), noun phrases (entity_profile, deep_research, recent_alerts), and inconsistent prefixes, so no single naming convention holds across the server.

Tool Count2/5

35 tools is too many for the apparent purpose, especially for a server named Virustotal. The bulk of the tools address unrelated Pipeworx research, memory, and prediction-market functions, so the count does not reflect the server's advertised domain.

Completeness1/5

For a VirusTotal server, only four lookup tools exist and there is no way to submit a URL/file, create a scan, retrieve analysis details, or explore relationships—core VirusTotal operations are missing. Scoped broadly, the unrelated Pipeworx tools are extensive, but they do not fill the gaps in the advertised domain. The surface is severely incomplete relative to the server name.

Resources