ARBITER

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

No annotations are present, so the description carries the full burden. It transparently discloses cost ($0.01 USDC), determinism, return type (MCP text content with JSON), and side-effect boundaries (does not generate or execute). This is strong behavioral disclosure, though it omits potential error/rate-limit details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact yet information-dense, covering purpose, usage, parameters, output, cost, and identity in a logical flow. No sentence is filler; the only slight extra is the MCP identity line, which is minor.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 explains the return format adequately. It also covers cost, determinism, and non-execution, making it complete enough for an agent to safely invoke the tool. Missing error scenarios are not critical for this simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes all parameters. The description adds useful semantic context by defining query as 'the situation to measure', candidates as 'finite caller-supplied field', and top_k as 'optionally limits returned results', which enriches the bare schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as measuring/ordering finite caller-supplied candidates against a query, using verbs like 'order' and 'measure'. It also distinguishes itself from generation by stating it does not generate candidates, which sets it apart from the sibling arbiter_embed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives an explicit condition: 'Use when a caller already has an explicit candidate field and needs it ordered...' It also notes the tool does not generate candidates or execute actions, which helps the agent avoid misuse. However, it does not explicitly name the sibling tool or give a detailed when-not-to-use list, so it's slightly below perfect.

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

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

With no annotations provided, the description carries the full burden, and it performs well. It discloses that the tool is paid, deterministic under identical inputs and engine state, does not store/retrieve/update/delete/rank records, and returns MCP text content containing JSON. It even states the exact price. This is far above baseline transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and each sentence carries useful information, including usage, non-behaviors, determinism, and cost. The 'MCP identity: fyi.grip/arbiter' line is arguably extraneous, but it does not detract much. It is dense but not bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter, no annotations, and no output schema, the description is remarkably complete. It explains the return format, cost, deterministic behavior, what it does not do, and how it differs from the sibling. No critical operational information appears to be missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since schema description coverage is 100%, the baseline is 3, and the description's added value is limited. It confirms that texts is 'one or more supplied strings', but this mostly restates the schema. No additional semantics like encoding, length limits, or formatting rules are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool converts supplied text into deterministic persistent machine representations, explicitly references the POST /v1/embed endpoint, and distinguishes itself from the sibling arbiter_compare. This gives the agent a precise operational picture and prevents confusion between the two tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: use this tool when representations are needed for storage, indexing, retrieval, clustering, reuse, or later comparison, and use arbiter_compare instead when ordering a finite candidate field is needed now. This is direct, actionable routing guidance.

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