162K English words, 74K lessons, knowledge graph, semantic reverse-dictionary search.
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Average 3.6/5 across 13 of 13 tools scored. Lowest: 2.4/5.
Most tools have clearly distinct purposes: dictionary lookups, reverse searches, translation, compliance checking, lesson generation, and knowledge graph exploration. The only ambiguity is generate_text, which overlaps with define_term and translate_word, but its role as a general text generation tool is clear from the description.
Naming is inconsistent, mixing verb_noun patterns (e.g., check_compliance, define_term, search_words) with get_* patterns (e.g., get_lesson, get_quiz) and the non-verb word_of_the_day. However, all names are descriptive and readable.
13 tools is well-scoped for a language platform covering dictionary, translation, lessons, quizzes, related words, images, and compliance. Each tool earns its place without redundancy or bloat.
The server provides comprehensive coverage for language-related workflows, including lookup, translation, lesson and quiz generation, related words, images, and compliance checks. Minor gaps like pronunciation audio or progress tracking exist, but core operations are well covered.
check_complianceAInspectCheck text for PII, profanity, reading level (Flesch-Kincaid), brand voice violations, GDPR/COPPA markers.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Text to check | |
| policy | No | Optional: {brand_voice: {tone, banned_words}, max_grade_level} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It correctly implies a non-mutating 'check' operation, but it does not explain what the tool returns (flags, scores, a report) or any additional processing behavior. This is a moderate gap for a tool with no output 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 a single, front-loaded sentence. It uses a clear verb, lists concrete check types, and contains no filler or redundancy. Every word 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?
The description covers the tool's overall capability and highlights the main compliance categories, and the schema documents both parameters including the nested policy object. The main missing piece is a description of the return format, especially because there is no output schema, but the current level is adequate for tool selection and invocation.
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 documents both 'input' and 'policy', so baseline is 3. The description adds meaningful context by naming Flesch-Kincaid reading level, brand voice violations, and GDPR/COPPA markers, which gives the agent a clearer understanding of what the policy object and output will address.
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 ('Check') and defines the resource ('text') plus the exact scope: PII, profanity, reading level, brand voice violations, and GDPR/COPPA markers. This clearly distinguishes it from the sibling dictionary/lesson tools, which have completely 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 implies when to use the tool by enumerating concrete compliance checks, but it does not explicitly state exclusion criteria or alternatives. However, since none of the sibling tools perform compliance checking, the usage context is clear enough without an explicit 'use when' statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
define_termAInspectGet an age-calibrated definition for any term. Known words use dictionary data; unknown terms use AI.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | Term to define | |
| age_group | No | Target age group | adult |
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 key behavioral traits: known terms use dictionary data, unknown terms use AI, and output is age-calibrated. It does not describe return format or potential unpredictability of AI, but for a definition tool this is sufficient 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?
Two concise sentences, front-loaded with the primary action and resource. Every word contributes meaning, and the fallback behavior is efficiently stated.
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 relatively simple, with no output schema. The description covers its core functionality and the dual-mode behavior, making it complete enough for an agent to select and invoke it. It does not explain return structure, but that is not critical for 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 'term' and 'age_group' already described. The description adds value by explaining 'age-calibrated', which clarifies how the age_group parameter affects the output beyond just 'Target age group'.
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' and resource 'definition for any term', with the key differentiator of 'age-calibrated'. It also clarifies the dual behavior for known vs. unknown terms, which distinguishes it from sibling tools like lookup_word.
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 clearly communicates when to use this tool: when an age-calibrated definition is needed. It does not explicitly exclude alternatives or name sibling tools, but the 'for any term' context and unique age-calibration make the intended usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explore_graphAInspectTrace a word through the knowledge graph to find connected lessons, themes, and related words.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | Word to trace through the graph |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The word 'trace' suggests a read-only, exploratory operation, and the description lists what the tool finds, but it does not disclose any side effects, permissions, error behavior, or what happens if the word is not found. This is acceptable for a simple tool but leaves some uncertainty.
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 that states the action and expected results without any waste. Every phrase contributes to understanding.
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 simplicity of the tool (one parameter, no output schema), the description adequately covers what it does and what it returns. It mentions the types of connected items (lessons, themes, related words), so the return expectation is clear. It could mention depth or traversal limits, but it is complete enough for a basic 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?
The only parameter, 'word', has a schema description that directly matches the tool description ('Word to trace through the graph'). With 100% schema coverage, the description adds little beyond the schema, but it reinforces the tracing context. Baseline 3 is appropriate.
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 'trace' with the resource 'knowledge graph' and explicitly names the outcomes: connected lessons, themes, and related words. This clearly distinguishes it from siblings like lookup_word or get_related_words, as it implies traversing relationships rather than simple lookup.
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 clear context for when to use this tool: when you need to find lessons, themes, and related words connected to a given word through the graph. It does not explicitly exclude alternatives, but the phrase 'trace through the knowledge graph' implies a unique use case, and no explicit when-not guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_textCInspectWrite, summarize, compliance-check, retrieve, negotiate, translate, or define text with policy enforcement.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| input | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only adds the vague phrase 'with policy enforcement' without specifying what policies are enforced, how they affect output, or side effects. There is no mention of safety, mutability, or task-specific behavior, leaving significant gaps.
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 sentence, reasonably concise and front-loaded with action verbs. However, 'policy enforcement' is vague and adds ambiguity without explanatory value, though it does not introduce waste or excessive length.
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's complexity (multiple task types), the lack of annotations and output schema makes this description insufficient. It does not explain return values, policy behavior, differences between tasks, or task-specific input requirements, leaving the agent under-informed for correct invocation.
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 description coverage is 0%, so the description must compensate for parameter meaning. It repeats the enum values for 'task' but does not clarify what the 'input' parameter should contain for each task (e.g., a prompt for 'write', source text for 'translate'), nor does it explain the expected format or constraints beyond the bare 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 lists multiple text operations (write, summarize, compliance-check, retrieve, negotiate, translate, define) but lacks a single clear primary verb+resource, making it ambiguous what the tool's core function is. It also fails to differentiate from sibling tools like check_compliance and translate_word that cover overlapping tasks.
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 no guidance on when to use generate_text versus its siblings (e.g., when to choose this over check_compliance or translate_word). It merely lists capabilities without context, prerequisites, or exclusions, leaving the agent without clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_lessonCInspectStructured 5-phase lesson (hook->story->wonder->action->wisdom). 365 days x 4 tracks x 3 age groups.
| Name | Required | Description | Default |
|---|---|---|---|
| age | No | ||
| day | No | ||
| track | No |
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 lesson structure but says nothing about behavior: whether it's a read-only operation, required permissions, return format, or error handling. This is a significant gap for a tool with no structured safety hints.
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 short sentences with no fluff, front-loading the core structure. The second sentence is cryptic but informative. It's concise without being under-specified to the point of uselessness.
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 3 optional parameters and no output schema, the description offers minimal context. It doesn't explain what a returned lesson contains beyond phase names, nor does it address potential edge cases (e.g., invalid day/track combos). It's barely adequate for a simple retrieval 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 description coverage is 0%, so the description must compensate. The phrase '365 days x 4 tracks x 3 age groups' aligns with the day (1-365), track (4 enums), and age (3 enums) parameters, adding product-level context. However, it doesn't explicitly map these dimensions to the parameter names or explain parameter syntax.
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 identifies the resource as a 'Structured 5-phase lesson' with a clear list of phases (hook->story->wonder->action->wisdom) and dimensions (365 days, 4 tracks, 3 age groups). This makes the tool's purpose apparent, though it relies on the tool name for the action verb. It doesn't explicitly distinguish from similar tools like get_quiz, so not a 5.
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 no guidance on when to use get_lesson versus alternatives such as get_quiz or other siblings. The dimensional information implies use cases but doesn't state them. No exclusions or alternative recommendations are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_quizCInspectAssessment questions from lesson content. 6 question types for formative evaluation.
| Name | Required | Description | Default |
|---|---|---|---|
| day | No | ||
| track | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behaviors. It does not mention side effects, permissions, return formats, or any operational traits—only the content of the questions.
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 brief and front-loaded, but it is a sentence fragment that omits critical information. It is concise but not well-structured for an agent, lacking the detail needed for reliable invocation.
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 unannotated parameters and no output schema, the description should compensate by explaining usage and expected results. It does not, leaving the tool incomplete for agent decision-making.
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 has two parameters (day, track) with no descriptions, and the description does not mention them at all. With 0% schema coverage, the description fails to add any meaning beyond the bare parameter names.
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 'Assessment questions from lesson content' clearly indicates the tool provides quiz questions, distinguishing it from sibling tools like get_lesson. However, it lacks an explicit verb like 'get' or 'retrieve', relying on the tool name for the action.
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 guidance is provided on when to use this tool versus alternatives. The description only states content and types, without any context about appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_word_imageAInspectGet all available images for a word - universal, culture-specific, and age-specific variants.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | Word to get images for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It does mention the types of images returned (universal, culture-specific, age-specific), which is useful context. However, it does not disclose potential error behavior, output format, or whether the operation is read-only (though 'Get' implies it), leaving some gaps for a completely self-contained description.
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 wording. It earns its place by conveying the tool's scope and output categories efficiently.
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 one parameter and no output schema, the description provides a clear purpose and scope. It could be improved by mentioning what the returned image data looks like (e.g., URLs, objects), but it is sufficiently complete for most use cases.
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 describes the sole parameter 'word' as 'Word to get images for', and the description adds no additional semantic detail beyond that. Since schema_description_coverage is 100%, the baseline of 3 applies.
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') and clearly identifies the resource ('images for a word'), including the scope ('all available... universal, culture-specific, and age-specific variants'). This clearly distinguishes it from sibling tools like define_term or lookup_word, which focus on definitions or general word data.
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 when you need images associated with a word, but it does not explicitly state when to prefer this over alternatives (e.g., lookup_word for definitions). No exclusions or alternatives are mentioned, but the tool's purpose is evident from the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_wordBInspectLook up any English word - returns IPA, definition, etymology, and translations in 47 dictionary languages for verified agent output.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | English word |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the transparency burden. It discloses the return payload (IPA, definition, etymology, translations) and language coverage (47 languages), which is useful. However, it does not explicitly state that the tool is read-only, does not mention output format or absence of side effects, leaving some ambiguity.
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 sentence that front-loads the verb and resource. It could be slightly tighter (the phrase 'for verified agent output' is vague and adds little clarity), but overall it is efficient and not padded.
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-parameter tool, the description lists the main return fields and language scope, which is useful. However, without an output schema, it omits details about the response structure (e.g., JSON format) and any limitations (e.g., case sensitivity or partial matches). It is adequate but not thorough.
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 'word' as 'English word' with 100% coverage. The description adds no additional meaning about the parameter's format, constraints, or examples, so it meets the baseline of 3 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 clearly states the tool's function: 'Look up any English word' and lists the specific data returned (IPA, definition, etymology, translations). It does not explicitly distinguish from sibling tools like define_term or translate_word, but the combined return fields imply a comprehensive lookup, so it earns a 4 rather than 5.
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?
There is no guidance on when to use this tool versus alternatives such as define_term, translate_word, or search_words. The description implies it is for full word lookups, but it never states that explicitly or provides exclusions, so it fails to guide tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_by_meaningAInspectFind an English word given a description of its meaning. Use when the user describes a concept but doesn't know the word. Returns words ranked by semantic similarity across 162,000 English words.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-25) | |
| query | Yes | Natural-language description of the concept (min 2 chars) |
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 useful behavioral context by stating results are 'ranked by semantic similarity across 162,000 English words,' giving insight into the ranking algorithm and corpus size. It does not mention any side effects, but as a read-only search tool, this is less critical.
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 purpose in the first sentence and usage guidance plus output characteristics in the second. Every sentence adds 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?
The description provides purpose, usage, and key output behavior, which is sufficient for a simple search tool with a fully documented schema. It lacks information about output format or error conditions, but these are not critical given the tool's simplicity and absence of an 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?
The input schema already covers both parameters with 100% coverage: query as a natural-language description and limit with default and range. The description does not add new parameter-specific details beyond what the schema states, so a baseline score of 3 is appropriate.
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 'Find' with the resource 'an English word' and specifies the input condition ('given a description of its meaning'), clearly distinguishing it from lookup tools like lookup_word or search_words.
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 states the use case: 'Use when the user describes a concept but doesn't know the word.' It does not name alternative tools but clearly implies when to prefer it over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_wordsBInspectSearch the dictionary by prefix, fuzzy match, or definition content. Find words matching a pattern or meaning.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query (min 2 chars) | |
| field | No | Search field | word |
| fuzzy | No | Enable fuzzy matching | |
| limit | No | Max results (1-50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only restates search modes already present in the schema. It does not explain output format, result limits, or any side effects, leaving the agent under-informed.
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 short and front-loaded, but the second sentence is largely redundant with the first. It's efficient overall, with only minor waste.
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 search tool with no output schema and no annotations, the description is under-specified. It doesn't explain what results look like, pagination, or how it differs from sibling search tools, making it's completeness below acceptable.
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 description coverage is 100%, so the baseline is 3. The description loosely maps to the 'field' parameter but adds no new semantics beyond what the schema already provides.
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 a dictionary search supporting prefix, fuzzy, and definition-content matching, using a specific verb and resource. It distinguishes itself from exact-lookup tools like lookup_word by emphasizing broad matching, though it doesn't explicitly name alternatives.
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 prefix, fuzzy, or definition-content searches, but provides no explicit when-to-use/not-to-use guidance or alternatives. It's minimally adequate for scoping the tool's purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
translate_wordAInspectTranslate a word or phrase. Single words use 47-language dictionary (instant). Sentences use AI translation.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Word or text to translate | |
| target_lang | No | ISO language code (es, fr, de, zh, ja, etc.) | es |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It adds meaningful behavioral traits by revealing that single words leverage a 47-language dictionary (instant) while sentences use AI translation, which is not obvious from the schema. It does not mention rate limits or permissions, but for a non-destructive translation tool, the disclosed behavior is sufficient to score above baseline.
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 short sentences with no fluff. The key verb and object are front-loaded in the first sentence, and the second provides necessary behavioral detail. Every word earns its place.
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 two parameters and no output schema. The description clearly states what it does and the two translation modes. Although it does not explicitly describe the return format, that is inferable for a translation tool. The schema covers parameters fully, and the description adds enough behavioral nuance to be complete for the given 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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining that the 'input' parameter's behavior depends on whether it is a single word or a sentence, affecting the translation engine used. This enriches the semantics of the input parameter beyond the schema's simple 'Word or text to translate'.
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 starts with a specific verb and resource: 'Translate a word or phrase.' It clearly distinguishes this from sibling tools like lookup_word or define_term by focusing on translation. It also adds behavioral nuance by splitting single words vs sentences, enhancing purpose clarity.
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 on when to use this tool: for any word or phrase translation. It adds usage guidance by explaining that single words use the dictionary and sentences use AI, which helps set expectations. However, it does not explicitly name alternative tools or state when not to use this tool, so it misses full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
word_of_the_dayAInspectGet the deterministic word of the day. Same word for all users on a given date. No auth required.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ISO date (YYYY-MM-DD). Defaults to today. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the result is deterministic, identical for all users, and requires no authentication. This goes beyond the basic 'get' verb and helps the agent understand safety and consistency, though it doesn't mention return format 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?
The description is two short sentences, each adding essential information: what the tool does and two key behavioral characteristics. It is front-loaded with the action ('Get') and contains zero fluff 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's simplicity (one optional parameter, no output schema, no annotations), the description sufficiently covers purpose, determinism, user consistency, and authentication requirements. It doesn't detail the exact return format, but 'Get the word' implies the outcome, and no output schema exists to specify further. Overall, it's complete enough for an agent to invoke it 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 input schema already has 100% coverage for the single optional 'date' parameter, describing it as an ISO date defaulting to today. The description reinforces the relevance of the date ('on a given date') but adds no new semantic details beyond the schema, hence the baseline score of 3.
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's function: 'Get the deterministic word of the day.' It identifies the specific resource (word of the day) and verb (get), and distinguishes it from sibling tools like lookup_word by emphasizing the deterministic, date-based nature.
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 (to retrieve the word of the day for a given date) and even notes it's the same for all users, which clarifies its utility. However, it doesn't explicitly mention alternatives or when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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