Blue Dot Brdg (BLDT)

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 disclose the return contents (summary, inclusions, page URL) and 'list' implies a safe read, but it doesn't state whether the list can be filtered, whether results are paginated, or that it is strictly non-mutating. Partial coverage for a zero-annotation 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?

A single front-loaded sentence with no filler; the entity and domain appear immediately. Slightly dense in its parenthetical branding but every clause (summary, includes, page) conveys output 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?

For a zero-parameter read tool with no output schema, the description adequately pre-describes the payload (summary, what it includes, the page to read). The only missing piece is a hint on how to pick this over recommend_services when the agent is deciding between browsing and recommending.

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 nothing for the description to disambiguate and no risk of an agent passing wrong arguments. Baseline 4 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?

States a specific verb ('List') and resource ('every service and client type Blue Dot Brdg publishes on bldt.in'), plus what each entry contains. An agent can tell this is a catalog enumeration. It doesn't explicitly name how it differs from recommend_services, but 'list every' vs 'recommend' is fairly self-evident.

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?

Usage is implied by the enumeration framing — fetch the full catalog rather than ask for a recommendation — but there is no explicit 'use this when... use recommend_services instead when...' guidance. With siblings that overlap in topic, one routing sentence would have raised this considerably.

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 the shape of the result ('best matches with their pages') and a meaningful business constraint ('every engagement is quoted after the diagnostic'), telling the agent no pricing comes back. It omits that this is a read-only/non-mutating lookup and says nothing about limits or matching behavior.

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

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

Three sentences, front-loaded with purpose, then return shape, then the quoting caveat. Nothing is redundant, though the third sentence sits slightly apart from the invocation guidance.

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 one-parameter, no-output-schema recommendation tool, the description covers what it does, what comes back, and a key downstream expectation. The only gap is the missing sibling comparison, which matters because list_services is adjacent.

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% and the single 'need' parameter is documented as free text in the user's own words, so baseline is 3. The description goes beyond that by exemplifying the kinds of needs to pass in, which helps the agent compose a good value rather than a keyword.

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?

Specific verb+resource: 'Recommend the Blue Dot Brdg services that fit a business need,' with three concrete scenario examples that make the intent unambiguous. It does not, however, distinguish itself from the sibling list_services, so an agent must infer the difference.

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 scenario examples (foreign brand entering India, Indian brand exporting, founder wanting end-to-end brand management) imply when the tool applies, but there is no explicit 'use this instead of list_services' routing or any exclusion. Usage is inferred rather than stated.

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 and discloses meaningful behavior: the request goes to an external team, the team responds by email, and nothing is booked or confirmed. It does not cover data retention or error behavior, but it gives the agent the key side-effect and response expectations.

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 three tight sentences with no wasted wording. It front-loads the action, then gives the consent condition and the response behavior.

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 seven-parameter submission tool with no annotations and no output schema, the description covers purpose, consent prerequisite, and what happens after the request. The main remaining gap is detailed parameter semantics, but the agent has enough context to understand the tool's overall behavior.

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 only 43%, with seven parameters, and the description mostly repeats the user_consented guidance already present in the schema. It does not explain the roles of name, company, email, phone, or service beyond what little is in the schema, so it does not compensate for the low coverage.

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 and resource: requesting an exploratory call from Blue Dot Brdg on the user's behalf. It is clearly distinct from the sibling service-listing tools because it submits the user's details to a team rather than reading service 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?

It gives an explicit gating condition: only use when the user has agreed to share details with Blue Dot Brdg and user_consented must be true. The description also sets expectations that this is not a booking. It does not name alternative tools, but the when-to-use and consent prerequisite are clear.

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