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Arcgis Lancaster

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

TDQS

A4.7/5.0
Behavior5/5

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

Annotations mark this as idempotent and non-destructive. The description adds meaningful context beyond annotations: scoping by identifier, persistent memory for authenticated users, 24-hour retention for anonymous sessions, and pairing with recall/forget. No contradictions 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?

Description is four sentences, front-loaded with the primary action, followed by usage context, technical details, and sibling pairing. No wasted words; every sentence adds value.

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, persistence/expiry, scoping, and relationship to sibling tools. Though there is no output schema, this is a simple save operation; the absence of return-value details is not a gap given the completeness of the rest.

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%: both 'key' and 'value' have detailed descriptions with examples. The description does not add parameter-specific semantics beyond what the schema already provides, so a 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 explicitly states the action ('Save data the agent will need to reuse later') with a specific resource ('key-value pair scoped by your identifier'). It also differentiates from sibling tools by mentioning 'Pair with recall to retrieve later, forget to delete.'

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: 'Use when you discover something worth carrying forward' with concrete examples (resolved ticker, target address, user preference, research subject). It also mentions alternatives (recall/forget) and clarifies persistence differences between authenticated and anonymous sessions.

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