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Arcgis Pittsburgh

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 the readOnly/idempotent annotations, the description reveals the embedding model (BGE-base-en), windowing (500-char overlapping), offset and similarity score returns, and the 200K char truncation with flagging. This gives the agent an accurate mental model of the tool's behavior without contradicting 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?

Four focused sentences: the first defines the action, the second explains the use case, the third pairs it with a sibling, and the fourth discloses technical constraints. No filler or repetition of schema field names.

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 there is no output schema, the description compensates by describing the return value (top-N passages with character offsets and similarity scores) and the truncation flag. The pairing with ask_pipeworx_grounded and the explicit 200K cap make it self-sufficient for an agent to decide when and how to invoke it.

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 coverage is 100%, so the baseline is 3. The description adds light context by calling text 'the text you already pulled' and providing query examples, but the schema already documents both params thoroughly; no meaningful new semantic information is added.

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 'Semantic search INSIDE a fetched record,' clearly specifying the verb (search) and resource (record). It distinguishes from siblings by explicitly pairing with ask_pipeworx_grounded and positioning itself as a sub-document search tool, unlike broader query tools.

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?

States 'Use when the record is too big to cram into the prompt,' giving a concrete trigger. Also advises pairing with ask_pipeworx_grounded and describes the workflow: 'fetch with the gateway, ground over the relevant passages instead of the whole document.' This effectively differentiates from alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Arcgis Pittsburgh toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Several near-duplicate tool clusters exist: ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), and ask_pipeworx_grounded overlap heavily, as do polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread. The three ArcGIS tools (search_datasets, layer_info, query_layer) are distinct, but their purpose is drowned out by the unrelated Pipeworx data tools.

Naming Consistency2/5

Most names use snake_case, but the pattern is inconsistent: some are verb_noun (ask_pipeworx, search_datasets, resolve_entity), some are noun_noun (layer_info, entity_profile, pipeworx_feedback), and some are adjective_noun (recent_alerts, recent_changes). Verbs are also inconsistent across similar actions (scan_ vs check_ vs compare_, and three different polymarket_ verbs plus a bare bet_research).

Tool Count1/5

34 tools is already heavy, but the server is named Arcgis Pittsburgh and only 3 of the 34 tools relate to Pittsburgh GIS data; the other 31 belong to unrelated domains (Pipeworx data lookup, prediction markets, memory, npm auditing). This is an extreme scope mismatch — the tool count is far too high for the stated purpose and mostly irrelevant noise.

Completeness2/5

For the ArcGIS Pittsburgh domain, the surface has basic read coverage (search datasets, inspect layer schema, query records) but no update/delete/write operations and no geospatial analysis tools, which are significant gaps for a GIS server. The broader tool set is a grab bag of research, prediction-market, and memory features that don't form a coherent lifecycle for any single domain, so completeness cannot be assessed as a unified surface.

Resources