mindmelt – B2B agency Frankfurt

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

Adds meaningful behavioral context: it operates against selection-rules.json and returns which good_fit/poor_fit criteria matched along with a score. However, with no annotations and no output schema, it doesn't state whether results are deterministic, how the score is scaled, or error behavior for non-matching input.

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 compact sentences, front-loaded with purpose and scope, followed by the parameter definition and return behavior. No filler, though the parameter line is somewhat redundant with the schema's type hint.

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

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

Covers purpose, source of criteria, and return content, which is adequate for a single-parameter tool. But with no annotations and no output schema, it omits the shape of the returned criteria/score and any failure modes, leaving gaps for a matching 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 carry the load. It does define the single parameter ('requirement: Freitext-Anforderung/Situationsbeschreibung des Interessenten'), clarifying format and source, but adds no length constraints or examples.

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?

States a specific verb+resource: a free-text match (Abgleich) of a requirement against documented good_fit/poor_fit criteria (signals) and services/capabilities. Distinguishes the resource (fit criteria from selection-rules.json) from siblings like find_cases or recommend_services, though it does not explicitly name an alternative tool.

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 it – when you have a free-text requirement and want to know fit – but does not state when NOT to use it or point to alternatives such as recommend_services for purely service-level matching versus this holistic fit check.

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

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

No annotations are provided, so the description carries the full burden. It does add real behavioral value by disclosing the matching semantics: industry supports exact or partial matching, while capability is matched exactly with no fallback to the services array. However, it says nothing about return format, permissions, or result limits.

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?

Front-loads the core action in the first sentence, then documents the two parameters compactly. No redundant filler, though the parenthetical caveat about services-array fallback could be tighter.

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 two-parameter filter tool with no annotations and no output schema, the description explains matching semantics but leaves the return shape (list of matching cases, fields returned, ordering) entirely unstated. Adequate but with a clear 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 0% (bare string properties with only titles), so the description must compensate and largely does: it explains that industry accepts exact or partial IDs (with an example) and that capability is an exact-match term against the capability field. It adds meaning well 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?

States a specific verb+resource: filtering cases.json by industry-ID and/or capability field. An agent can tell it targets the cases dataset, but it never names or contrasts with siblings like search_services or find_pages, so the differentiation is only implicit.

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 'und/oder' phrasing implies you can filter by either or both fields, which is usable guidance, but there is no explicit when-to-use-this-vs-alternatives or exclusion criteria (e.g. when to prefer search_services instead).

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

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

No annotations provided, so the description carries full burden. It discloses what the tool searches and what it returns (url, title, description, markdown-URL, hreflang-Alternativen, capabilities), which is helpful. However, it doesn't describe pagination, result limits, or performance characteristics for a search tool.

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

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

Well-structured with a clear first sentence describing the tool's function, followed by parameter documentation in a list format. However, it could be slightly more concise; the parameter explanations are necessary given the 0% schema coverage.

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, no annotations, and four parameters with 0% schema coverage, the description does a good job documenting parameters and return fields. It is largely complete, though it could mention result limits or pagination behavior for a search 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. It documents all four parameters: query (with examples and default behavior), sub_brand (with enumerated values), type (with possible values), and lang (with values). It adds meaningful semantics like 'Leer = alle Seiten' and specific allowed values that aren't in 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?

States a specific verb (durchsucht) and resource (vollstaendiger Seitenindex von mindmelt.de ueber Title, H1, Meta-Description und URL). Specifies which data sources are searched (pages.json, alle Sitemap-URLs inkl. Sub-Brands und englischer Version), clearly distinguishing it from sibling tools like check_fit or get_company_profile.

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?

Implies usage context (searching the page index) but doesn't explicitly state when to use this tool versus alternatives like check_fit or get_company_profile. No exclusions or conditions for choosing this over siblings.

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

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

No annotations are provided, so the description carries the full burden. It reveals the data source (capabilities.json) and that exactly one entry is returned, but says nothing about read-only semantics, behavior for an unknown id (error vs empty), or what fields the returned entry contains.

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?

Very short and front-loaded: the purpose sentence comes first, followed by the parameter hint. The second line slightly restates the parameter name but earns its place by giving example values.

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 one-parameter lookup with no output schema, the essentials are present, but the missing behavior for invalid ids and the unknown domain of valid id values leave real gaps 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 coverage is 0%, so the description must compensate. It supplies two concrete example ids ('seo', 'webentwicklung'), which meaningfully clarifies the expected value format, but it does not indicate the full set of valid ids or whether the lookup is case-sensitive.

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?

States a specific verb (delivers/returns) and resource (a single entry from capabilities.json keyed by id). It is clearly a single-item lookup, distinguishable in kind from list/search siblings, though it does not explicitly name any sibling alternative.

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 on when to use this tool versus siblings like get_company_profile, get_pricing, or recommend_services. The agent must infer that it is a raw lookup of one capability by id, with no stated exclusions or prerequisites.

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

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

No annotations are provided, so the description carries the full behavioral burden. The 'get_' prefix implies a read, but the description says nothing about permissions, whether the profile is scoped to the caller's own company, caching, or freshness of the bundled data.

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 front-loaded sentence with zero filler. It is efficient, but the compression comes at the cost of clarity ('entities.json organization + people gebuendelt'), which borders on cryptic rather than merely concise.

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?

There is no output schema, so the description should carry more of the return-value story. It does state that the profile bundles organization and people, which is the minimum needed, but gives no detail on structure, scope, or size for a tool whose entire job is returning data.

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 takes zero parameters, so there is no parameter semantics to explain; per the rubric this yields a baseline of 4. The description correctly implies that no input is required to retrieve the profile.

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 names a verb ('Liefert') and the resource content (organization + people bundled as a company profile), so the purpose is decipherable. However, it leans on opaque internal jargon ('entities.json') rather than describing the returned concept in plain terms, and it makes no effort to distinguish itself from siblings like get_capability or get_pricing.

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, no prerequisites, and no mention of alternatives among the sibling tools. The agent must infer usage entirely from the tool name and the terse one-liner.

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

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

With no annotations, the description must carry the behavioral burden, and it does disclose useful traits: the backing file, that omitting parameters dumps the entire file, the id-over-sub_brand precedence rule, and that three-tier entries return a full tiers array. It omits error behavior for unknown ids, permissions, and any read-only framing.

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?

Purpose is front-loaded in one line, followed by per-parameter notes; the examples earn their place by showing exact expected string values. It is slightly longer than strictly necessary but nothing is 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 two-optional-parameter lookup with no output schema, the description adequately conveys the return shapes (full file with stand/hinweis/entries, or a tiers array). Only failure modes for a bad id are left unaddressed.

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 is the only source of parameter meaning, and it delivers: sub_brand is explained with concrete example values, and id is explained with an example plus its precedence over sub_brand and its effect on the returned tiers structure.

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?

States a specific verb and resource ('Liefert Preisinformationen aus pricing.json') and specifies the exact data source, which clearly separates it from siblings like get_capability or get_company_profile. It does not explicitly name any sibling tool, so it stops short of full differentiation.

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?

Gives clear context for each call mode: no parameters returns the whole file, sub_brand filters, and id selects a single entry with explicit precedence over sub_brand. It never states when to prefer this tool over the sibling pricing-related tools, but the invocation conditions themselves are unambiguous.

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

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

No annotations are provided, so the description carries the full behavioral burden. It does not disclose matching algorithm, ranking behavior, result limits, whether multiple services are returned, or error behavior. It mentions linked capabilities, which is a small plus, but far more disclosure is needed for a recommendation tool with no 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?

Two short parts: one sentence for the core behavior and one for the parameter with an example. No waste, front-loaded purpose. Slightly informal formatting due to line break, but efficient.

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 recommendation tool with no output schema and no annotations, the description omits critical context: how matching works, what a 'service' object contains, ranking, and whether it requires exact terms. It is incomplete 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?

Schema description coverage is 0%, so the description must compensate. It does explain the single parameter as a free-text problem description and gives an example (B2B relaunch with SEO and AI visibility). This is helpful but minimal given 0% schema coverage; the baseline of 4 for zero params does not apply since there is one param.

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?

States a specific verb+resource: matches a problem description against 'problem' fields in services.json and returns best-matching services plus linked capabilities. This distinguishes it from search_services, though the mechanism (fuzzy keyword matching) is not fully specified. Clear purpose overall.

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 have a free-text problem statement, but does not explicitly state when to use this versus search_services, find_cases, or check_fit. Context is implied rather than spelled out.

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

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

With no annotations, the description carries the full burden. It does disclose real behavioral traits: the matching mode is keyword/alias rather than vector search, and results are sorted with a score across capabilities and services. However, it says nothing about authentication, result limits, or pagination, leaving gaps for a search tool.

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 short lines, front-loaded with the operation and scope, followed by parameter guidance and return content. Little waste, though the line breaks make it slightly list-like rather than prose.

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?

No output schema exists, and the description partly compensates by stating results are sorted hits from capabilities and services with a score. Combined with the clear search scope and parameter example, an agent has enough to invoke it correctly; only pagination/limits are unaddressed.

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% and the single query parameter has no schema-level documentation, so the description must compensate. It does so by defining query as Suchbegriff(e) and giving concrete examples ("SEO Relaunch", "KI Sichtbarkeit"), which clarifies the expected input format.

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?

States a specific verb (Volltextsuche) and the exact resources searched (capabilities.json, services.json, aliases from entities.json), plus a negative scope (kein Vector-Search). An agent can distinguish this keyword/alias matcher from siblings like find_pages or get_capability without opening any schema.

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 the tool applies (broad keyword lookup over capabilities/services/aliases) and rules out vector-search semantics, but never names an alternative sibling or states an explicit when-not condition or prerequisite. Usage is inferable rather than guided.

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