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Glama

Solscan

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?

Beyond annotations, it reveals embedding model, window size, character cap with truncation flagging, and offset for verification. 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?

Concise 4-5 sentences, front-loaded with core purpose, followed by technical details and pairing advice. No redundant text.

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?

Covers purpose, usage context, technical details, limits, and integration with sibling tool. Complete for an agent to understand when and how to use.

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?

Schema covers all 3 parameters with full descriptions. The description adds minor clarifications (e.g., query examples) but does not significantly extend schema info.

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 'Semantic search INSIDE a fetched record' with specific examples (SEC 10-K, article) and pairs with 'ask_pipeworx_grounded', distinguishing its role.

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 tells when to use ('record too big to cram into the prompt') and suggests an alternative workflow with 'ask_pipeworx_grounded'.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx, and multiple prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage) overlap in purpose. The Solana-specific tools are distinct, but the large non-Solana cluster creates real ambiguity.

Naming Consistency3/5

All tools use snake_case, but naming styles vary widely: get_/list_ verbs, ask_pipeworx family, polymarket_* cluster, and descriptive noun-style names like entity_profile, validate_claim, generate_llms_txt, scan_dependency. There is no single consistent verb_noun convention across the set.

Tool Count1/5

With 36 tools, the count is high, but the critical problem is scope mismatch: a server named Solscan has only 5 Solana-related tools, while 31 are unrelated data/research/prediction-market utilities. This makes the tool count inappropriate for the apparent purpose.

Completeness2/5

As a Solana explorer, the set covers only account details, token holdings, token metadata, transactions, and transfers; major gaps include blocks, token price/history, NFTs, programs/staking, and more comprehensive transfer history. For the broader data-research theme, coverage is broad but scattered and lacks a single coherent domain.

Resources