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Arcgis Lancaster

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

A4.8/5.0
Behavior5/5

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

Description adds significant context beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K character cap with truncation flag, and return payload details. This complements the readOnlyHint/idempotentHint without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is longer than ideal but every sentence carries useful information: core function, use case, pairing advice, technical details, and limits. Structured in a logical flow, but slightly dense.

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?

With no output schema, the description properly explains return values (top-N passages with offsets and scores) and edge cases (truncation flag). Given the tool's complexity, this is fully sufficient for an agent to use 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% with good parameter descriptions, and the description enhances meaning by providing concrete query examples, default limit, and the truncation behavior tied to the text parameter. This adds 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 clearly states the tool performs semantic search inside a fetched record, with specific inputs (text, query) and outputs (passages with offsets and scores). It explicitly references sibling? not directly but contrasts with entire document retrieval 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?

Provides explicit when-to-use guidance: when the record is too big to fit in the prompt, saving context. It names an alternative (ask_pipeworx_grounded) and explains the pairing pattern, offering clear contextual direction.

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 overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve as query/discovery entry points. Polymarket tools also blur together (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk). While many tools have distinct purposes, these overlapping clusters create real misselection risk.

Naming Consistency2/5

All names are snake_case but the pattern is inconsistent: some are verb_noun (ask_pipeworx, search_datasets, validate_claim), some are noun_verb (query_layer, layer_info is noun_noun), and some are single vague words (forget, recall, remember). No consistent verb_prefix or resource_suffix convention, and the mix of meta-tools vs data tools makes the naming feel arbitrary.

Tool Count2/5

34 tools is far too many for a server ostensibly named 'Arcgis Lancaster' — only 3 tools relate to GIS. The bulk is an unrelated general-purpose data/prediction-market toolkit, making the count excessive for the apparent scope. Even as a broad data toolset, 34 tools is on the heavy side and would benefit from splitting into focused servers.

Completeness2/5

The tool surface is a grab bag with no coherent domain, so completeness is hard to assess and clearly lopsided. GIS functionality has search/query/info but no lifecycle management, while the data side has many query/analysis tools but no create/update/delete operations except for subscriptions and memory. Obvious gaps exist for a 'Lancaster' server (e.g., no layer creation, editing, or spatial analysis tools), and the unrelated tools make the set feel incomplete for any single stated purpose.

Resources