Read-only MCP access to DSGHT.ai published foresight and country-grounded claim resolution.
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Average 4.2/5 across 10 of 10 tools scored.
Each tool targets a distinct function: text analysis (detect_tensions, extract_assumptions), retrieval of published foresight (get_public_report, etc.), listing available resources (list_countries, list_public_spaces), searching (search_spaces), and macro claim resolution (resolve_country_claim). Even similar-sounding tools like detect_tensions and get_public_tensions are clearly differentiated by purpose (user-provided text vs. published analysis).
All tool names follow a consistent verb_noun pattern with underscores: detect_tensions, extract_assumptions, get_country_indicator, get_public_report, get_public_scenarios, get_public_tensions, list_countries, list_public_spaces, resolve_country_claim, search_spaces. The verbs are specific and the nouns are descriptive, making the naming predictable and easy to understand.
With 10 tools, the server covers its domain—strategic foresight and macro indicators—without being overwhelming. Each tool serves a clear purpose, and the count is appropriate for the scope: text analysis, resource listing, retrieval, search, and claim resolution. There is no unnecessary bloat or deficiency.
The tool set covers the core workflows: analyzing user-provided strategy text, retrieving published foresight reports/scenarios/tensions, listing available spaces and countries, searching, and resolving macro claims. A minor gap is the lack of a tool to retrieve a specific extracted assumption (though extraction is stateless by design). Overall, the surface is largely complete for a public-facing server.
detect_tensionsAInspectUse when the user pastes strategy text and asks what is internally inconsistent: detects the structural contradictions — where the text quietly bets on two things that cannot both hold. Stateless taster; nothing is stored. DSGHT surfaces the conflict rather than smoothing it over.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Strategy / plan / memo text to analyze (plain text). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses statelessness ('Stateless taster; nothing is stored') and output style ('surfaces the conflict rather than smoothing it over'), though no annotations exist to complement.
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 with no fluff, though the phrase 'DSGHT surfaces the conflict' could be clearer to those unfamiliar with the acronym.
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 stateless tool with one parameter and no output schema, the description fully covers purpose, usage, behavior, and output style.
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 a clear parameter description. The tool description reinforces the purpose but adds little new parameter-specific meaning 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?
Description clearly states the tool detects structural contradictions in strategy text, using specific verbs ('detects', 'surfaces') and distinguishing it from siblings like 'extract_assumptions' by focusing on internal inconsistency.
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 the user pastes strategy text and asks what is internally inconsistent', providing clear context, but does not mention when not to use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_assumptionsAInspectUse when the user pastes a strategy, plan, or memo and wants its hidden bets made explicit: extracts the falsifiable, load-bearing assumptions (dated/quantified bets + structural findings). Stateless taster — nothing is stored; the persistent per-company assumption registry with monitoring and a Brier track record is the authenticated tier.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Strategy / plan / memo text to analyze (plain text). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key behavioral traits: statelessness ('nothing is stored') and that it produces 'dated/quantified bets + structural findings'. However, it does not mention error handling, rate limits, or auth requirements, though the tool's simplicity mitigates this.
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, front-loaded with usage context. Every sentence serves a purpose: first defines when to use, second states behavior and distinguishes from alternatives. No redundant or filler content.
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 (1 param, no output schema, no annotations), the description adequately covers purpose, usage, and key behavioral traits. The lack of output format details is a minor gap, but the description implies the output structure via 'falsifiable assumptions (dated/quantified bets + structural findings)'.
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%, baseline is 3. The description adds value by specifying the expected content type ('plain text') and context ('strategy / plan / memo text to analyze'), enriching the schema's minimal description. There are no enums or nested objects to complicate.
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 specific verb-resource ('extracts the falsifiable, load-bearing assumptions') and clearly states the input (strategy/plan/memo). It distinguishes from sibling tools like 'detect_tensions' and a persistent registry, making the purpose 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?
Explicitly states when to use: 'Use when the user pastes a strategy, plan, or memo and wants its hidden bets made explicit'. Also provides exclusion context by contrasting with a persistent assumption registry, helping agents decide between tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_country_indicatorAInspectLook up a country's authoritative macro indicator (inflation | gdp_growth) directly — deterministic, offline, cited. Use whenever you need a real macro figure instead of recalling one from memory: give the jurisdiction (CZ, DE, US, ...) and indicator, plus an optional year (omit for the full annual series).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | optional 4-digit year, e.g. 2022 | |
| indicator | Yes | ||
| jurisdiction | Yes | ISO code, e.g. CZ, DE, US |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses key behaviors: deterministic, offline, cited. This helps the agent understand the tool's reliability and side-effect-free nature, though more detail on citations or response format could enhance 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?
Single, front-loaded sentence conveys purpose, behavior, usage, and parameter options with no redundancy. Every part 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?
For a simple lookup tool with three parameters and no output schema, the description covers purpose, usage, parameters, and key behaviors. Missing return format details, but acceptable given tool simplicity.
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 covers 67% of parameters with descriptions (year and jurisdiction). Description adds clarity that year is optional and omitting returns full series, but does not explain indicator values beyond enum names. Adequate but not exceptional.
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 clearly states verb ('Look up') and resource ('country's authoritative macro indicator'), specifies indicators (inflation, gdp_growth), and distinguishes from memory recall, making purpose 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?
Explicitly states when to use ('whenever you need a real macro figure instead of recalling one from memory') and provides parameter guidance. Lacks explicit when-not-to-use or alternatives, but context from siblings makes this less critical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_public_reportBInspectGet the latest full foresight report (markdown) for a published future-space. Use when the user wants the detailed analysis, or a quotable, cited source on a covered topic.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | future-space slug from list_public_spaces |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states it gets the report, without mentioning authentication, error handling, or what 'latest' means. Behavioral traits like idempotency or speed are absent.
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: one for definition, one for usage guidance. No wasted words; front-loaded 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?
The tool is simple with one parameter and no output schema. The description covers purpose and usage but omits details like return format (markdown) specifics, error cases, and prerequisites beyond the slug hint.
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 description adds value beyond the schema by specifying 'future-space slug from list_public_spaces', telling the agent to use list_public_spaces first. With 100% schema coverage, this context is useful but not essential.
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 it gets the latest full foresight report in markdown for a published future-space. It distinguishes itself from siblings like get_public_scenarios and get_public_tensions by specifying 'report', but does not 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 provides explicit guidance: 'Use when the user wants the detailed analysis, or a quotable, cited source on a covered topic.' However, it does not mention when not to use this tool or compare directly with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_public_scenariosAInspectGet the scenario matrix with probabilities for a published future-space. Use when the user asks how a domain (banking, energy, CEE markets, ...) might play out, what the plausible futures are, or wants to reason across scenarios instead of a single forecast.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | future-space slug from list_public_spaces |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as read-only nature, destructive potential, auth needs, rate limits, or error handling. The description only covers purpose and usage, not behavior beyond that.
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: first clearly states purpose, second gives usage guidance. No redundant or filler content. Front-loaded with key 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?
For a simple tool with one param and no output schema, the description covers purpose and usage adequately. However, it lacks behavioral transparency (e.g., return format, safety) and does not describe output structure, which is absent in schema. With no annotations, this is a 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?
Schema description coverage is 100%, so the schema already documents the 'slug' parameter. The description does not add any extra meaning beyond implying the slug comes from 'list_public_spaces', which aligns with the schema. Baseline score 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 explicitly states the tool retrieves a 'scenario matrix with probabilities for a published future-space', with a clear verb ('Get') and resource. It distinguishes from siblings like 'detect_tensions' and 'extract_assumptions' by specifying the use case for scenario reasoning.
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 when-to-use guidance: 'when the user asks how a domain might play out, what the plausible futures are, or wants to reason across scenarios instead of a single forecast.' It implies context but does not explicitly state when not to use or list alternatives, though the sibling list helps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_public_tensionsAInspectGet the structural tensions — the load-bearing contradictions — for a published future-space. Use when the user asks what forces conflict, what could break a strategy, or what the tensions/paradoxes are in a covered domain. DSGHT surfaces contradictions rather than averaging them.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | future-space slug from list_public_spaces |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It includes a behavioral note ('DSGHT surfaces contradictions rather than averaging them'), but does not disclose read-only nature, authentication requirements, or side effects. Some information is given, but gaps remain.
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, each serving a distinct purpose: defining the tool and specifying usage. There is no unnecessary elaboration, making it efficient and front-loaded with critical information.
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 retrieval tool with no output schema, the description covers purpose, usage, and parameter origin. It does not describe the output format, but given the simplicity, the current level is 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?
The schema already describes 'slug' with a note linking to list_public_spaces. The description reinforces this by stating 'future-space slug from list_public_spaces', providing clear context for the parameter. Since coverage is 100%, the description adds value by confirming the source.
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 explicitly states it retrieves structural tensions for a published future-space, using precise language like 'load-bearing contradictions'. It distinguishes from sibling tools such as 'detect_tensions' by focusing on retrieval of existing tensions rather than creation or analysis.
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 concrete use cases (e.g., 'what forces conflict', 'what could break a strategy'). While it doesn't explicitly state when not to use or list alternatives, the context is clear enough for an agent to decide appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_countriesAInspectList the jurisdictions and macro indicators (inflation, GDP growth) available to resolve_country_claim and get_country_indicator, with per-country year coverage and source. Call this to check coverage before resolving a claim or looking up an indicator.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the output content (jurisdictions, indicators, coverage, source) and purpose. It is a read-only listing with no side effects, though it does not explicitly state read-only or authentication needs; but with no annotations, this is sufficient.
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 efficient sentences: first states purpose and output, second gives usage guidance. No wasted words, front-loaded with key information.
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 no output schema, the description fully explains the return value (jurisdictions, indicators, coverage, source) and its use case relative to sibling tools. Completely adequate for a parameterless listing 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 tool has zero parameters and schema coverage is 100%, so baseline is 4. The description adds value by explaining what the output contains, which compensates for the lack of parameters.
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 it lists jurisdictions and macro indicators (inflation, GDP growth) available to specific sibling tools, with per-country year coverage and source. The verb 'list' is specific and the resource is well-defined, distinguishing it from siblings.
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 'Call this to check coverage before resolving a claim or looking up an indicator,' providing clear context on when to use the tool and its relation to related tools. No exclusions needed for a coverage check tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_public_spacesAInspectList the DSGHT future-spaces published for public use (slug, topic, status, depth). Call this first to discover what foresight is available before fetching scenarios, tensions, or a report.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It indicates a read-only list operation and specifies the returned fields, but does not mention authentication requirements, rate limits, or whether the list is paginated. For a simple public list, this is adequate but could be more explicit.
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: the first states purpose and result fields, the second provides usage context. Every word is necessary; 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?
For a simple list tool with no parameters and no output schema, the description covers purpose, fields, and usage. It could mention the return format (e.g., list of objects) but the level of detail is sufficient for an agent to use 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 input schema has zero parameters, so schema description coverage is 100% (vacuous). The description adds value by explaining what the tool returns, which is sufficient for a parameterless tool. Baseline with 0 params is 4.
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 it lists DSGHT future-spaces published for public use, with specific fields (slug, topic, status, depth). It distinguishes from siblings by positioning itself as the first step before fetching scenarios, tensions, or a report.
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 to call this tool first before other tools like get_public_scenarios, get_public_tensions, or get_public_report. This gives clear context on when to use it, though no alternative or exclusion criteria are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_country_claimAInspectUse this WHENEVER the user states or asks you to verify a numeric macro claim (inflation, GDP, interest rates): settle it against the claim's OWN country's authoritative series instead of answering from memory, which risks the wrong jurisdiction (e.g. U.S. CPI for a Czech claim). Binds the claim to a jurisdiction (CZ, DE, ...) and returns a cited above/below verdict. Deterministic and offline.
| Name | Required | Description | Default |
|---|---|---|---|
| subject | Yes | e.g. "inflation", "GDP growth" | |
| direction | Yes | ||
| threshold | Yes | ||
| resolve_by | No | ISO date the claim resolves by, e.g. 2022-12-31 | |
| jurisdiction | Yes | ISO code, e.g. CZ, DE |
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 that the tool 'returns a cited above/below verdict' and is 'deterministic and offline'. This covers the key behavioral aspects—output format, no external dependencies, and deterministic nature. No contradictory information.
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 long, with the key usage front-loaded. It conveys necessary context without extraneous content. The second sentence is slightly dense but acceptable for the information density required.
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 no output schema, the description adequately explains return value (cited above/below verdict). It covers the main use case, parameter intent, and behavioral traits. The tool has moderate complexity (5 params), and the description provides sufficient context 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?
Schema description coverage is 60% (3 of 5 parameters have descriptions). The description reinforces that 'subject' and 'jurisdiction' are used in context, but does not add meaning beyond the schema for parameters like 'resolve_by' or 'threshold'. It partially compensates but does not fully cover the missing 40%.
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 specific verb ('resolve a claim') and resource ('numeric macro claim... against the claim's OWN country's authoritative series'). It distinguishes this tool from siblings by emphasizing that it settles claims with authoritative data rather than relying on memory, which could cause jurisdictional errors.
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 when to use the tool ('WHENEVER the user states or asks you to verify a numeric macro claim') and warns against answering from memory to avoid wrong jurisdiction. It does not explicitly name alternatives or list when not to use it, but the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_spacesAInspectFind the most relevant published DSGHT future-space by keyword (topic + report text, plain-text ranked, no AI). Use this to locate a space before calling get_public_scenarios / get_public_tensions / get_public_report when you do not already know the slug.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | search terms, e.g. "open banking consent" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations exist, the description carries full burden. It discloses the search is plain-text ranked and does not use AI. It also mentions it searches topic and report text. However, it does not address potential behavioral aspects like rate limits, authentication needs, or what happens if no results are found. Still, the disclosed details are informative and non-contradictory.
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, each adding value. It front-loads the purpose and follows with usage context. No redundant or extraneous information. It is efficiently communicated.
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 one parameter and no output schema, the description covers core functionality well. It names sibling tools that follow from this search. However, it does not mention the output format (e.g., returns slugs, names, or a list) or error handling. This minor omission prevents a perfect score, but in context, the description is still very useful.
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 one parameter 'query' with a brief description. The tool description adds significant meaning by explaining that the query searches topic and report text, and that ranking is plain-text based without AI. This goes beyond the schema's generic description, fully enhancing parameter understanding.
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 searches for published DSGHT future-spaces by keyword, specifying it searches topic and report text, and distinguishes it from sibling tools by noting it's used before other space-specific tools. The verb 'find' and resource 'published DSGHT future-space' are specific and 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?
Explicitly tells when to use this tool: 'Use this to locate a space before calling get_public_scenarios / get_public_tensions / get_public_report when you do not already know the slug.' This provides clear context and exclusion guidance, helping the agent decide between this and alternatives.
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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