Proximens Oracle

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

Adds significant context beyond annotations: describes fetch+analyze behavior, fast vs deep modes with timing, and deep mode timeout fallback. Annotations already signal read-only and open-world; description enriches with operational 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?

Well-structured with clear sectioning (INPUT, RETURNS, USE). Slightly verbose on deep mode fallback description, but overall efficient and front-loaded.

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 output schema exists, description covers all necessary aspects: input format, mode options, return structure including fallback, and usage guidance. Complete for a tool of this complexity.

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% lowering burden, but description adds value: explains mode trade-offs, branche_hint format and fallback, max_issues default. Goes beyond schema repetition.

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?

Clearly states the tool fetches and audits a web page against GEO engine principles. Distinguishes from siblings like bulk_search and compare_urls by focusing on single URL audit.

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?

Provides explicit guidance: 'USE fast mode for quick checks and bulk triage; USE deep mode when you need a client-ready audit report.' Notes free tier blocked. Does not explicitly mention when not to use, but context is clear.

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?

Annotations already cover read-only, idempotent, non-destructive. Description adds return format details (results per query, totals, processing time) and input constraints, enhancing behavioral context.

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?

Description is compact: purpose first, then parameter details, return format, and usage guidance. No redundant sentences; every sentence adds value.

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?

Covers input, output, and usage well. Missing details on category filter values (could reference list_categories) and error handling, but overall sufficient for a bulk search tool with output schema.

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 0%, but description explains 'queries' (array of natural-language strings, length constraints), 'top_k_per_query' (range and default), and 'category filter' (though not listing enum values). Adds meaning beyond schema types.

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 'Run many GEO-principle searches in a single fast call' and distinguishes from sibling 'search_principles' by noting it avoids 'repeated search_principles calls'.

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?

Explicitly says 'USE WHEN you need many lookups at once, e.g. a full-site audit or a keyword list, instead of repeated search_principles calls', providing clear when-to-use and alternative.

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?

Discloses return format (JSON with scores, principles, violations, insights) and that it uses the same engine as audit_url. No contradiction with annotations (readOnlyHint, openWorldHint).

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?

Two sentences plus structured INPUT/RETURNS section. No wasted words, front-loaded with purpose and key details.

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?

Tool has only two simple parameters and output schema exists (referenced). Description covers usage, inputs, outputs, and differentiators. Fully adequate.

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?

Both parameters have descriptions in schema, and description adds value by noting both are required and http/https format. Schema coverage is 100%.

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?

Clearly states it compares two URLs against GEO Engine principles, differentiates from sibling audit_url. Specific verb 'compare' and resource 'URLs' with clear scope.

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?

Explicitly says 'USE WHEN you want a competitive GEO gap analysis' and contrasts with audit_url. Provides clear context for when to choose this tool.

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?

Annotations already establish readOnly, idempotent, and non-destructive hints. The description adds concrete behavioral context: it returns a single principle with specific fields, and notes tier-dependent additional fields. This goes beyond the annotations without contradicting them.

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?

Three sentences, front-loaded with the primary purpose, then logically structured with INPUT, RETURNS, and USE WHEN. Every sentence provides distinct value with no redundancy.

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 simple single-param fetch tool, the description fully covers the use case, return format, tier differences, and typical workflow. An output schema exists, so return values need no elaboration, but the description still supplies it. No gaps in necessary context.

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?

The input schema already documents id with 'Principle UUID (from search_principles results)' at 100% coverage. The description repeats this and adds 'normally taken from a prior search_principles result,' a minor enrichment. Baseline 3 is justified because the schema carries the semantic weight.

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 opens with 'Fetch one GEO principle from the Proximens GEO Engine by its UUID,' naming a specific action (Fetch), the resource (one GEO principle), and the identifier method (UUID). This clearly differentiates it from siblings like search_principles or get_stats.

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 explicitly states 'USE WHEN you already have a principle id and need its full detail — typically to drill down after search_principles.' This provides strong contextual timing but does not explicitly list alternatives or when-not-to-use scenarios, so a 4 is appropriate.

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?

The description adds valuable behavioral context beyond the annotations: it discloses that the data is 'live aggregate statistics,' explains the distinction between total_principles and full corpus size on Pro/Enterprise, and mentions the last-validated timestamp. This gives the agent insight into what the results represent and when they might be incomplete.

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 and well-organized: it leads with the core purpose, then clearly labels INPUT, RETURNS, and USE WHEN sections. Every sentence adds necessary information without redundancy or fluff.

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 simple tool with no parameters and rich annotations, the description covers the essential aspects: what it returns, when to use it, and the availability caveat for Pro/Enterprise. It is fully sufficient for an agent to invoke it correctly and interpret the results.

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?

With zero parameters, the schema already fully covers the absence of inputs, and the description confirms 'INPUT: none.' No additional parameter explanation is needed, matching the baseline of 4 for no-parameter tools.

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 opens with a specific verb-resource pair: 'Return live aggregate statistics for the Proximens GEO Engine knowledge base.' This clearly distinguishes it from sibling tools like 'audit_url' or 'search_principles' which perform different actions.

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 includes an explicit 'USE WHEN you need to gauge the size and quality of the corpus before relying on it,' providing a clear use case. However, it does not name any alternatives or specify when not to use this tool, so it stops short of the highest level of guidance.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Beyond that, the description discloses the exact output schema (categories array with category, count, description), sorting by count, and a reconciled total that matches get_stats.total_principles. This adds valuable behavioral context about return structure and consistency.

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 with sections (INPUT, RETURNS, USE WHEN) and is front-loaded with its purpose. The category list is useful but adds length; overall it is concise but slightly verbose, earning a 4 rather than 5.

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 simple, zero-parameter read-only tool with output schema and annotations, the description fully covers purpose, output format, category enumeration, and use case. It also includes a consistency guarantee with get_stats.total_principles, leaving no meaningful gaps.

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?

Tool has zero parameters, and the input schema states 'No input parameters' (100% coverage). Description confirms 'INPUT: none.' Per the baseline for 0 parameters, this is a 4; the description does not need to add parameter details.

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?

Description opens with a specific verb+resource: 'List the GEO principle taxonomy of the Proximens GEO Engine with a live count of high-confidence principles per category.' It clearly distinguishes from siblings by noting the categories can be used to narrow a search_principles call.

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?

Explicit 'USE WHEN' clause: 'USE WHEN you want to discover which categories exist before narrowing a search_principles call with the category filter.' This names the alternative tool (search_principles) and gives a concrete scenario, satisfying the need for when/when-not guidance.

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?

Annotations already cover readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is known. The description adds meaningful context: the knowledge base is curated, continuously updated, has 4,000+ principles with confidence scores and source tracing, and Pro/Enterprise tiers return additional fields (full_text, source). This goes beyond annotations to clarify behavior.

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 with INPUT, RETURNS, and USE WHEN sections. Each sentence conveys essential information: knowledge base characteristics, parameter constraints, output format, and usage context. No filler or repetition.

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 the tool has 4 parameters, an output schema, and rich annotations, the description is complete: it explains what the knowledge base is, input constraints, return fields with tier differences, and when to use it. It leaves little ambiguity for an agent deciding whether to invoke this 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% with detailed parameter descriptions for query, top_k, category, and min_confidence. The description summarizes these, including the same min_confidence advice already in the schema ('Default 0.5 filters noise; raise to 0.8+ for high-confidence claims only'). It adds the semantic-search framing but no new parameter-level meaning.

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 opens with 'Semantic search over the Proximens GEO Engine', a specific verb+resource pair that clearly identifies the operation. It uniquely positions this tool against siblings like proximens_geo_audit_url or proximens_geo_get_principle by emphasizing search over a structured knowledge base of GEO principles.

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 'USE WHEN' clause provides explicit guidance: 'when you need evidence-backed answers about how AI search engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot) select, rank and cite web content.' This clearly separates it from audit/compare tools, though it does not explicitly name alternative tools or list when not to use.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds that it returns JSON with specific structure (H1, H2, FAQ, schema markup), which is useful but does not disclose additional behavioral traits like auth requirements or error handling.

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?

Four concise sentences: purpose, input, output, usage. No fluff. Each sentence adds distinct value, front-loaded with the core action.

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 4 parameters, output schema description, and annotations, the description covers inputs, outputs, and usage. Missing minor details like error states or rate limits, but overall largely complete for an API 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 0%, so description must compensate. It does so by paraphrasing all parameters with ranges, defaults, and constraints (e.g., 'one of 7 verticals', '300-5000, default 1500', 'up to 3 competitor_urls'). This adds readability and context beyond the raw schema.

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?

Description explicitly states it generates a structured GEO-optimized content brief. It differentiates from sibling tools like audit, bulk search, etc. by specifying the use case: 'USE WHEN you need to brief a writer so a page is built to be cited by AI search engines.'

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?

Description provides clear when-to-use guidance via 'USE WHEN...' and lists input constraints. It does not include negative guidance (when not to use) or explicitly compare to siblings, but the context is clear enough.

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