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Arcgis Princewilliam

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?

While annotations already indicate idempotent (true) and non-destructive (false), the description adds significant behavioral context: key-value storage scoped by identifier, persistence for authenticated users, and a 24-hour retention for anonymous sessions. These details go beyond the annotations and help the agent understand side effects and lifetime.

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 four sentences, each adding necessary information: purpose, usage examples, storage mechanics, and complementary tools. It is front-loaded with the primary action and contains no redundant or filler content.

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?

For a simple two-parameter write tool, the description covers purpose, usage, persistence, scoping, and relationships to sibling tools. Annotations and schema provide the remaining necessary details, making the description complete for an agent to select and invoke the tool 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 both 'key' and 'value' parameters well-defined with examples. The description adds only general context ('key-value pair') but no additional parameter-specific semantics, so the baseline score of 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 tool's function with a specific verb ('Save') and resource ('data the agent will need to reuse later'), and distinguishes it from siblings by explicitly mentioning 'recall' and 'forget' as complementary tools. Examples of use cases (resolved ticker, target address, user preference) make the purpose concrete.

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?

The description provides explicit guidance on when to use the tool ('Use when you discover something worth carrying forward') and names alternatives ('Pair with recall to retrieve later, forget to delete'), creating a clear usage context that differentiates it from sibling tools.

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

A3.7/5.0
Disambiguation3/5

The set mixes several overlapping clusters: three ask_pipeworx variants (beta is explicitly identical to stable right now), six Polymarket tools with similar opportunity-scanning purposes, and two AI-visibility tools that duplicate each other. However, the descriptions are detailed enough that an agent can usually pick correctly, so the ambiguity is moderate rather than severe.

Naming Consistency2/5

Tool names follow no single convention — some are verb_noun (query_layer, validate_claim), some noun_noun (entity_profile, layer_info), some company-prefixed clusters (pipeworx_*, polymarket_*), and a few standalone verbs (forget, recall). While snake_case is consistent, the absence of a uniform verb_noun pattern across the set makes it unpredictable.

Tool Count2/5

At 34 tools, the server is oversized for its apparent purpose, and the count is even more problematic because most tools belong to a general Pipeworx/data platform while only 3 serve the 'Arcgis Princewilliam' GIS theme. The set feels like two unrelated servers merged, with many tools earning no clear place in a unified product.

Completeness2/5

The GIS side is a read-only stub (search, schema, and query) with no editing or feature-level retrieval, and the broader Pipeworx side has a notable dead end: tools return pipeworx:// citation URIs but no tool is provided to fetch those resources. The result is a surface that is simultaneously over-built in prediction markets and under-built in its namesake domain.

Resources