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Arcgis Chapelhill

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses rich behavioral details beyond the annotations: embedding model (BGE-base-en), similarity method (cosine), windowing (500-char overlapping), and input cap (200K chars with truncation flagging). It also explains that returned passages carry offsets for verbatim quote verification, which is not captured in annotations or schema.

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 concise yet information-dense, typically 3 sentences. It front-loads the core purpose, then usage, then technical details. Every sentence adds value without fluff, achieving an ideal balance between brevity and completeness.

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?

Despite having no output schema, the description adequately explains the return format (passages with offsets and similarity scores). It covers the core use case, behavior, and limitations (truncation). Combined with the rich parameter schema and annotations, the tool is well-specified for an agent to select and invoke correctly.

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%, with clear descriptions for all three parameters (text, limit, query) including examples. The description does not add substantially new parameter semantics—it only restates the 200K char cap (already in schema) and the 'top-N' concept (already in limit description). Thus baseline 3 is appropriate.

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 verb and resource: 'Semantic search INSIDE a fetched record.' It specifies the inputs (text + query) and outputs (top-N passages with offsets and similarity scores). It also distinguishes from siblings by pairing with ask_pipeworx_grounded and focusing on searching within already-fetched content.

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 also provides complementary guidance: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This clarifies the workflow and alternatives.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently literally identical, and the polymarket_* family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) all orbit the same prediction-market opportunity space. The descriptions are detailed and do help, but the sheer number of near-synonymous entry points makes misselection likely.

Naming Consistency4/5

The vast majority of tools follow a consistent snake_case, verb-first pattern: ask_pipeworx, compare_entities, query_layer, resolve_entity, search_datasets, validate_claim. Minor deviations exist — the polymarket_* tools are noun-phrase style and a few names like entity_profile, layer_info, and ai_visibility_check are noun-led — but the overall style is uniform enough to be predictable.

Tool Count2/5

34 tools is already in the overstuffed range, but the bigger problem is that only 3 of them (search_datasets, layer_info, query_layer) relate to the server's stated ArcGIS/Chapel Hill identity. The other 31 tools appear to be an unrelated Pipeworx data-research, prediction-market, memory, and subscription bundle merged into this server, making the count grossly disproportionate to the apparent purpose.

Completeness2/5

The three GIS tools form a usable read-only search → schema → query workflow, so the ArcGIS domain is not completely absent. However, as a whole the server has no coherent domain to be complete for, and the ArcGIS side lacks broader capabilities like layer enumeration, spatial filters/statistics, or any write/edit operations. For a server named Arcgis Chapelhill, having 31 out-of-scope tools constitutes a major completeness failure.

Resources