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Arcgis Peoria

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant behavioral detail: returns passages with character offsets and similarity scores, uses BGE-base-en embeddings with cosine over 500-char overlapping windows, and has a 200K char cap with truncation flagging. This richly informs the agent about execution semantics beyond the 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?

Three sentences with clear structure: purpose first, usage scenario second, technical mechanics third. Every sentence adds value with no fluff or repetition. It is front-loaded and appropriately sized for the tool's complexity.

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?

The description covers what the tool returns (passages with offsets and scores), the technical approach (embeddings, windows), the input cap, truncation behavior, and how it pairs with sibling tools. With good annotations and complete schema, this is sufficient for correct selection and invocation.

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 description coverage is 100%, so baseline is 3. The schema already describes all parameters in detail (text max length, query examples, limit range/default). The description reinforces these but doesn't add new parameter-level meaning. It mentions top-N and offsets, but that's return value context, not param semantics.

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 opens with a specific verb+resource: 'Semantic search INSIDE a fetched record.' It clearly distinguishes from sibling tools by emphasizing that it operates on text the agent already has, not fetching new data. The examples (SEC 10-K, article) and pairing with ask_pipeworx_grounded further clarify its unique 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 states when to use: 'Use when the record is too big to cram into the prompt.' It names a complementary alternative (ask_pipeworx_grounded) and explains the workflow: fetch with the gateway, then ground over relevant passages. This gives clear usage context and an implied when-not (when the record fits in the prompt).

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

Many tools are very similar (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and serve the same purpose with slight variations, making it hard for an agent to choose correctly. Additionally, the tool set mixes completely unrelated domains (ArcGIS geospatial vs. Pipeworx/Polymarket data), further confusing the purpose of each tool.

Naming Consistency2/5

Tool names follow multiple conventions: ask_pipeworx uses snake_case, while layer_info and query_layer use snake_case as well but with a different pattern. There is no consistent verb_noun pattern across the set; some are descriptive (validate_claim) while others are vague (process, run). The mix of conventions and lack of a unified naming scheme hurts predictability.

Tool Count1/5

At 34 tools, the count is excessive for a server supposedly focused on ArcGIS Peoria. Only 3 tools (layer_info, query_layer, search_datasets) are actually related to geospatial data, while the other 31 are from external services (Pipeworx, Polymarket). This mismatch suggests the server is extremely poorly scoped.

Completeness1/5

For a geospatial server, the tool set is severely incomplete. It lacks basic GIS operations like spatial filtering, editing, or analysis. The three geospatial tools only provide schema discovery and simple attribute queries. Meanwhile, the bulk of the tools cover a completely different domain (data lookup, prediction markets), leaving the core domain almost entirely unaddressed.

Resources