raptr-agent-builder

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds value beyond that by disclosing the concrete validation surface (manifest, naming, class, perform method, secrets) and the fact that it emits fixable findings, though it says nothing about failure modes or how findings are shaped.

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 tight sentences with zero filler: the rule inventory is front-loaded and the usage loop follows. Every clause 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 two-parameter validation tool with no output schema, the description conveys the check scope and that it returns actionable findings via 'fix anything it reports'. A minor gap remains on exactly what a pass/fail response looks like, but the annotations cover the safety dimension.

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 both parameters (code as full Python source, filename ending in _agent.py) are already fully documented in the schema. The description adds only the framing that the input must be a 'finished' file, which is marginal beyond the schema's own text.

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 (checks) and resource (a finished agent file) and enumerates the exact rule set it validates: manifest, naming, class, perform method, and hardcoded secrets. This clearly separates it from retrieval siblings like get_agent_code, get_agent_template, and find_agents, which fetch rather than validate.

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 an explicit trigger ('call it on every agent you write') plus a workflow loop ('fix anything it reports, and call it again until it passes'), which is real actionable guidance. It stops short of a 5 because it names no alternative tool or any condition under which you would skip it.

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?

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint, so the safety profile is covered. The description usefully discloses registry scope and size (open, ~1,700 single-file agents), but says nothing about rate limits, result freshness, or what a match actually returns. Modest added value over the structured 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?

Two compact sentences with the core purpose front-loaded; every clause does work. Minor redundancy in restating 'RAR' twice back-to-back ('(RAR) (RAR, about 1,700...)'), which is slightly sloppy but not costly.

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 discovery tool with no output schema, the description should hint at what matches look like (agent name, description, code reference) since the agent must decide what to do next with results. It covers when to call well but leaves the return shape and follow-up path (e.g., get_agent_code) unstated.

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 both parameters are already documented, including the example 'summarize sales calls' for query. The description reinforces the query intent with example user utterances but adds no format or semantics beyond the schema, so baseline 3 is right.

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 ('Searches the open agent registry (RAR)') and scopes it precisely ('about 1,700 single-file agents'). The framing as finding 'agents that already do what the user wants' lets an agent distinguish it from siblings like get_agent_code or use_agent_here without opening a 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?

Gives explicit triggers: 'Use it before building from scratch, or when the user asks whether an agent exists for a task,' plus sample phrasings like 'is there an AI tool for...'. It does not name alternative tools for when the user already knows an agent by name, so it stops short of full alternative routing.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description usefully adds that the whole source is returned rather than metadata, but says nothing about behavior when the name is unknown or about any size 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?

One sentence with zero waste, front-loading the return value and the lookup key before the supporting example. Nothing needs trimming.

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

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

With no output schema, the description carries the burden of describing the return, and 'full source' covers it adequately for a single-parameter read tool. Minor gaps are error behavior and whether output is raw text or structured.

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 'name' parameter is fully documented in the schema with an example. The description's parenthetical example (@bill/neuron_agent) reinforces the format but adds no semantics beyond the schema, so baseline 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?

States a specific verb and resource (returns the full source of one agent from the registry), names the lookup key (its name), and gives the exact identifier format. An agent can tell this apart from get_agent_template and check_agent 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 phrase 'so it can be read or adapted' implies the use case but never states when to choose this over siblings like get_agent_template, find_agents, or check_agent. Guidance is implied rather than explicit, with no exclusions.

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?

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), and the description adds genuinely useful behavioral context — that the caller must fill in the template itself and must not show it to the user before running check_agent. It stops short of describing template format or size.

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-loaded with the trigger scenario followed by the return value and the ordered next steps. It is slightly dense and runs several clauses together in the first sentence, but every sentence carries actionable 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?

With no parameters, no output schema, and annotations carrying the safety profile, the description supplies everything an agent needs: when to call it, what it returns, and the mandatory follow-up sequence. No meaningful gap remains.

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?

Zero parameters, so there is nothing for the description to compensate for; baseline 4 applies. The description correctly implies the tool takes no input and the content is derived from the conversation.

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 ('Returns the official single-file agent template and its rules') and clearly distinguishes itself from siblings by naming check_agent and use_agent_here as the downstream steps. An agent knows exactly what this call produces.

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 explicit trigger conditions (building an AI agent/assistant/bot from an idea, repetitive task, process description, or meeting transcript) with concrete examples, plus an explicit ordered workflow: fill in the template, then check_agent, then use_agent_here. Nothing is left to inference.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the prerequisite gating and the 'free Brainstem' framing, but says nothing about what the tool returns or how the guidance is delivered, so it adds only moderate context beyond annotations.

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 tight sentences with no waste; the optional/later framing and the prerequisite are front-loaded where an agent will see them before deciding to offer the tool.

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 read-only instructional tool with two optional params and no output schema, the description covers the essential decision context. It could note that the result is guidance text to relay to the user, but nothing needed to call it correctly is missing.

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%, with both optional params (os enum, filename) fully documented in the schema. The description adds no syntax or format detail for either parameter, so the baseline 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 states a specific resource and scope: instructions for keeping an agent running on the user's own computer via the free Brainstem. It implicitly distinguishes itself from siblings like use_agent_here and get_agent_code by being the local-hosting guide, though it never names those siblings explicitly.

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 a clear timing condition ('Optional, for later') and a prerequisite ('Only offer this after the user has used the agent in the chat and wants to keep it'). It does not name alternative tools for related needs, 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.

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

Annotations declare a non-read-only, non-destructive, closed-world write, and the description adds meaningful context beyond that: only the request text is stored, no name or contact data, and user consent must be obtained first. It stops short of describing what happens after the request is recorded or any confirmation 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 short sentences, front-loaded with the usage condition, then the privacy scope, then the consent gate. Nothing is redundant and every sentence carries actionable 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 write tool with no output schema, the description covers trigger, consent, and data scope adequately. Only minor gaps remain, such as what the agent should do if the person says no or what happens after the request is recorded.

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 parameter's description already says 'as they agreed to send it.' The description's phrasing mirrors that constraint rather than adding format, length, or fidelity details beyond the schema, so baseline 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 states the tool records a request the person agrees to send, which is a specific verb+resource, and implicitly distinguishes it from siblings by framing it as the fallback when none of 'these tools' can fulfill the need. It is clear, though the purpose is conveyed through usage framing rather than a direct statement.

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 gives the trigger condition ('wants something none of these tools can do and says yes to passing the request on') and a prerequisite ('Ask before calling it'). The agent knows exactly when to invoke it and what consent gate must be cleared first.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false; the description reinforces this with 'Nothing is published by this tool' and adds non-obvious behavior — that it scans the supplied code for emails, phone numbers, and secrets. It does not describe output format, but with no output schema that is a minor gap.

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, no filler, and the triggering condition is front-loaded so the agent sees the routing rule first. The remaining sentences each add distinct information (scan behavior, publishing path, safety guarantee).

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 read-only two-parameter tool with no output schema, the description covers what the tool does, what it does not do ('nothing is published'), and what the user gets back (instructions for publishing under their own GitHub account). Adequate, though it never states the response shape explicitly.

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% for both parameters (filename, code), so the schema already carries the semantics. The description adds no syntax, format, or constraint details beyond what the schema states, which is the baseline 3 case.

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 concrete flow: validate the agent, scan it for personal details (emails, phone numbers, secrets), then explain publishing to the RAR registry. This is distinguishable from siblings like find_agents or get_agent_code. However, the opening clause 'Checks the agent' overlaps with the sibling check_agent without naming it, so the boundary between the two tools 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?

Gives an explicit gating condition: 'Use only when the user says they want to share an agent they built.' The word 'only' functions as a when-not constraint, and the rest of the description clarifies the scope of the flow. An agent knows precisely when to select this tool rather than its 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered and the description does not contradict it. The description adds real behavioral context beyond the annotations: what is returned (a short Python runner), that nothing needs installing, and the no-Python fallback. It omits execution/return-format details of the runner itself, so not a 5.

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 front-loaded with the primary trigger and the primary outcome, then adds the fallback instruction. Sentences are dense but each carries information; the 'Ask the user for their real data, run the agent, and show the result' sentence is slightly prescriptive/duplicative but still actionable.

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

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

With one optional parameter, full schema coverage, no output schema, and annotations covering the safety profile, the description covers what an agent needs: when to invoke, what it returns, and what to do without Python. Naming the sibling check_agent as the upstream step closes the main routing gap; only the exact usage of the returned runner is left implicit.

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?

There is only one parameter (filename) and schema description coverage is 100%, so the schema already documents its meaning and expected format ('invoice_triage_agent.py'). The description adds no further semantics about the filename (e.g. where it comes from or whether it must match the checked agent), so 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 states a specific outcome: it 'Returns a short Python runner so you can run the agent in this chat with your own Python tool on the user's own data.' This clearly distinguishes it from siblings like check_agent and get_agent_code by naming the concrete artifact returned. It stops short of a 5 because the verb ('use') is generic and the differentiation from get_agent_code/how_to_run_agent is only implied.

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 explicit triggering conditions ('right after an agent passes check_agent, or whenever the user wants to try an agent') and names check_agent as the upstream step. It also supplies a fallback path ('If Python is unavailable, apply the agent's logic yourself'). No explicit 'do not use this when...' exclusion against other siblings, so not a 5.

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