Frontier-design reference: file formats, converters, definitions, sensors, companies, design jobs.
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Average 3.9/5 across 7 of 7 tools scored. Lowest: 3.1/5.
get_definition and identify_format both cover file formats, and identify_format also lists conversions, overlapping with convert_file. However, each tool has a distinct primary purpose (definition lookup vs. file identification vs. conversion discovery), so the boundaries are reasonably clear but not perfect.
All tools use a verb_noun pattern (compare_formats, convert_file, get_company, get_definition, identify_format, list_design_jobs) with the exception of 'search' which is a bare verb. This minor deviation is acceptable.
7 tools is within the ideal 3-15 range. Each tool covers a distinct aspect of the reference domain, from search to specific lookups to job listings, without redundancy.
The server covers the stated domain well: search for full-text, get_definition for terms/technologies/sensors, identify_format and compare_formats and convert_file for file formats, get_company for companies, and list_design_jobs for careers. The only notable gap is a dedicated tool for standards or sensors, but get_definition and search cover them adequately.
compare_formatsAInspectCompare two file formats (by slug, extension, or filename) — extensions, category, and a verdict on when to choose each. Use for 'X vs Y' / 'difference between X and Y'.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the operation (comparison), accepted input forms (slug, extension, or filename), and the output contents (extensions, category, verdict). It does not mention error handling or edge cases, but for a simple comparison tool this is adequate.
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 two sentences, front-loaded with the core purpose and result, and ends with concrete usage examples. Every word earns its place with no redundancy or filler.
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 low complexity (2 simple string params, no annotations, no output schema), the description fully covers purpose, accepted input formats, output contents, and usage context. It is complete enough 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no descriptions for parameters a and b (0% coverage), so the description must compensate. It states that both parameters are file formats identified by slug, extension, or filename, giving meaningful context for both params. It does not separately describe each param, but since they are symmetric inputs this is sufficient.
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 uses the specific verb 'Compare' with the resource 'two file formats' and clearly states what is produced: extensions, category, and a verdict. It distinguishes itself from siblings like identify_format (singular identification) and convert_file (conversion) by focusing on side-by-side comparison.
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 says 'Use for X vs Y / difference between X and Y', giving clear context for when the tool is appropriate. It does not name alternative tools or state when not to use it, so it falls short of a full 5 but is still strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_fileAInspectFind the in-browser converter (if any) that turns one format into another, e.g. from='fbx' to='glb'. Returns the tool URL and notes, all client-side (files never upload). Use for 'how do I convert X to Y'.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | ||
| from | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the operation is client-side ('files never upload'), states that it returns a tool URL and notes, and notes that the converter may not exist ('if any'). This provides important behavioral context beyond the schema.
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 two sentences, front-loaded with the primary action and example, and contains no redundant information. Every sentence adds value: the first defines the tool's function, the second clarifies usage and a key behavioral trait.
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 two string parameters and no output schema, the description covers the essential aspects: what it does, how to use it, what it returns, and a privacy-relevant behavior. It could mention error cases or lack of converter, but the 'if any' clause partially covers that.
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 the description must compensate. It explains that `from` and `to` are formats and gives a concrete example ('fbx' to 'glb'), adding meaning to the otherwise bare string parameters. However, it does not enumerate accepted format values or provide additional parameter details, leaving some ambiguity.
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 the tool finds an in-browser converter for turning one format into another, with a concrete example (from='fbx' to='glb'). It also specifies the output (tool URL and notes), distinguishing it from sibling tools like compare_formats or identify_format which serve different purposes.
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 says 'Use for how do I convert X to Y', giving clear context for when to apply the tool. It does not explicitly mention alternatives or exclusions, but the usage directive is specific and actionable enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_companyBInspectProfile of a frontier company: what they build, why it matters for frontier design, competitors and technologies from the knowledge graph.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose whether this is a read-only operation, what happens if the slug is invalid, or any rate limits/auth requirements. It only describes the returned content, not the 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?
A single, well-structured sentence that front-loads the core purpose. Every word earns its place, though it could be expanded to include parameter guidance without losing concision.
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?
The description gives a reasonable overview of the returned profile content, but with no output schema and no annotations, it leaves gaps around the slug parameter, usage scenarios, and expected response format. Adequate but not complete.
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 schema has a single 'slug' parameter with no description. The tool description mentions 'frontier company' but does not explicitly explain what 'slug' represents or how to obtain it, leaving the parameter semantics unclear.
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 this tool retrieves a profile of a frontier company, including what they build, why it matters, competitors, and technologies from the knowledge graph. This specific verb+resource distinguishes it from siblings like get_definition or search.
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?
No explicit guidance on when to use this tool vs alternatives. It does not mention that it should be used when you know a company slug, nor does it contrast with search or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_definitionAInspectGet the definition and reference copy for a dictionary term, technology, sensor, or file format by slug (use search to find slugs).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| family | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does not explicitly state that this is a read-only operation, nor does it mention any side effects, error handling, or output details beyond the vague 'definition and reference copy'. The word 'Get' implies read-only but does not explicitly disclose it. This is a significant gap for a tool with zero annotation coverage.
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 a single, front-loaded sentence with no redundant words. Every phrase adds value: 'by slug' clarifies the lookup key, and the reference to 'search' is actionable. It is a model of concise, direct writing.
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?
With no output schema, the description gives a high-level idea of the return ('definition and reference copy') but not the structure or format. It also misses the 'standard' enum value and provides no information about error cases or prerequisites. For a simple lookup tool with only two parameters, this is a minimally viable description, but it leaves several 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?
The schema has 0% description coverage, so the description must compensate for parameter meaning. It gives meaning to 'slug' (find via search) and 'family' (lists categories dictionary, technology, sensor, file format). However, it omits the 'standard' value from the enum and does not explain that 'family' must take one of the enumerated values, leaving the mapping incomplete.
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 uses a specific verb ('Get'), clearly identifies the resource ('definition and reference copy'), and names the categories (dictionary term, technology, sensor, file format). It also distinguishes itself from the sibling 'search' tool by instructing to use 'search' to find slugs, making its purpose and scope unambiguous.
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 provides clear context for when to use the tool: when you need a definition/reference copy for a known slug. It explicitly directs users to 'search' for slugs, which is a useful alternative/companion. However, it does not explicitly mention when NOT to use this tool (e.g., comparing formats or identifying a format from a file), so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
identify_formatAInspectIdentify a file format from an extension, filename, or slug (e.g. 'step', 'scan.mcap', 'gltf') and get how to open or convert it: what it stores, its extensions/MIME/spec, whether a free in-browser viewer exists, and available client-side conversions. Use for 'what is this file' / 'how do I open X'.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | An extension, filename, or format slug |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the exact kind of information returned (storage, extensions/MIME/spec, viewer availability, conversions), making behavior predictable. It doesn't mention handling of unknown formats, but the read-only nature is strongly implied by 'identify' and 'get how to open or convert it'.
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 tightly written sentences: the first states purpose and examples, the second lists the output contents and common use cases. No redundant words or filler.
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 single-parameter identification tool, the description covers input forms and the output domain thoroughly. There is no output schema, so the description appropriately fills in return-value expectations. It could mention what happens for unknown formats, but that is a minor gap.
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 schema already describes the single parameter as 'An extension, filename, or format slug' (100% coverage). The description adds concrete examples ('step', 'scan.mcap', 'gltf') and clarifies the accepted forms, adding value beyond the 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?
The description clearly states a specific action ('Identify a file format') with input sources (extension, filename, slug) and detailed output contents. It also naturally distinguishes itself from siblings like convert_file and compare_formats.
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?
It explicitly tells the agent when to use it: 'Use for "what is this file" / "how do I open X".' It does not explicitly name alternatives or when-not-to-use, but the use-case guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_design_jobsAInspectLatest design/UX/HMI roles at frontier companies (defense, robotics, space, industrial, health), refreshed from company job boards.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| industry | No | Industry slug (e.g. defense, robotics) — omit for all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds the behavioral context that data is 'refreshed from company job boards', implying periodic updates. However, it does not mention ordering, pagination, rate limits, or other operational behaviors.
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 a single, front-loaded sentence with no filler. It states the purpose, target industries, and data source efficiently, earning high marks for conciseness and structure.
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?
The tool is simple (2 params, no output schema), and the description gives enough context for basic invocation and selection. It lacks output format details and limit semantics, but for a list tool, the description is largely 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?
Schema coverage is 50% (industry documented, limit not). The description reinforces the industry parameter by listing specific categories, but does not explain the 'limit' parameter's semantics, default, or constraints. It provides some added meaning but leaves a key gap.
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 uses a specific verb ('list') and resource ('design/UX/HMI roles at frontier companies'), and adds scope (defense, robotics, etc.) and data source (company job boards). This clearly distinguishes it from broad sibling tools like 'search'.
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 implies the tool is for fetching current design job listings, but provides no explicit 'when to use' guidance, alternatives, or exclusions. Sibling tools like 'search' could be an alternative, but no differentiation is offered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspectFull-text search across Falcon's frontier design reference: terms, technologies, file formats, sensors, companies, tools, standards.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavior. It only states the scope of the search but does not describe how results are returned, whether there are limits or pagination, how relevance is determined, or any other behavioral trait. This is a significant gap for a tool that performs an open-ended search.
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 a single, well-structured sentence that front-loads the core purpose and scope. Every word contributes meaning, with no fluff or 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?
The tool is simple with one parameter, and the description accurately defines its scope. While it lacks return-format details, the absence of an output schema and the straightforward nature of a full-text search make the description reasonably complete. It could benefit from mentioning result ranking or filtering, but it is adequate for the tool's 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?
The schema covers the single parameter with a generic 'Search query' description, and the tool description adds some context by indicating what the query searches across. However, it does not provide syntax, format, or example values beyond what the schema already states, so it meets the baseline but does not exceed it.
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 uses a specific verb 'search' and clearly identifies the resource: 'Falcon's frontier design reference'. It also enumerates the types of content covered (terms, technologies, file formats, etc.), which distinguishes it from sibling tools that perform more specific actions like compare, convert, get, or identify.
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 implies usage for general discovery across the reference, but it does not explicitly state when to choose this over alternatives like get_definition or identify_format. No exclusions or alternative suggestions are provided, leaving the guidance implicit.
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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