Plan and build a physical body for your AI agent: boards, parts list, assembly and pairing.
Scored across 10 tools
Most tools have clearly distinct purposes: device lookup (assess_hackability), planning (intake_gadget/plan_gadget), and build plans (get_build_plan) don't overlap. The firmware cluster (find_firmware_playbooks vs get_firmware_playbook, and prepare_firmware_job vs verify_firmware_artifact) is related and could momentarily confuse, but descriptions clarify find=discovery vs get=retrieval by id.
Every tool follows a consistent verb_noun snake_case pattern: assess_hackability, check_firmware_compatibility, find_firmware_playbooks, get_build_plan, intake_gadget, plan_gadget, prepare_firmware_job, verify_firmware_artifact. No camelCase or style mixing.
Ten tools is well-scoped for a domain split between device hackability assessment and agent-body planning. Each tool maps to a distinct step in the workflow (discover, check, plan, prepare, verify) with no redundant entries.
The firmware lifecycle is well covered end-to-end (find playbooks, check compatibility, get playbook, prepare job, verify artifact), and the gadget planning flow is complete (intake to plan). Minor gaps: no way to enumerate/list catalog devices or build plans beyond a known id, which limits discovery.
Look up a device by id, exact name or substring and return hackability, brick risk, firmware links, curated firmware playbooks, community size, build page and agent platform support. Input: device_name 1-200 chars.
| Name | Required | Description | Default |
|---|---|---|---|
| device_name | Yes | Device name or id, 1-200 characters. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden, and it does communicate that this is a read-style lookup whose output spans several derived attributes. However it says nothing about permission/auth requirements, what happens when a substring matches multiple devices, or whether results are cached — genuine behavioral gaps for an identifier-resolution 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?
The core sentence is dense and front-loaded with the lookup action followed by the payoff fields. The trailing 'Input: `device_name` 1-200 chars.' line is pure duplication of the schema and earns no 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?
An output schema exists, so the return-value enumeration is technically redundant, and the tool is low-complexity with a single required parameter. What is missing is routing guidance against the many sibling tools that expose the same underlying data; for a one-param lookup the description is adequate but not self-sufficient.
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 schema already documents device_name with length bounds, so the baseline is 3. The description adds one genuinely new detail — that substring matching is allowed, not just exact names/ids — but otherwise restates the schema's '1-200 chars' constraint verbatim.
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 ('look up a device by id, exact name or substring') and enumerates the returned artifacts (hackability, brick risk, firmware links, playbooks, community size, build page, platform support). This is clear, but it never names the sibling tools it overlaps with (get_build_plan, find_firmware_playbooks), so an agent gets no explicit disambiguation from those tools.
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 when-to-use guidance and no alternatives mentioned, despite five of the nine siblings covering sub-capabilities this tool bundles (playbooks, build plan, firmware compatibility). The agent must infer on its own whether to call this aggregate lookup or the narrower sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fail-closed compatibility check for one firmware playbook. Required model, codename, revision, SoC or firmware identifiers must exactly match a curated target; missing facts return unknown and mismatches return unsupported.
| Name | Required | Description | Default |
|---|---|---|---|
| observed | Yes | ||
| playbook_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses fail-closed behavior, the exact-match requirement, and the outcome semantics (missing facts → unknown, mismatches → unsupported). It omits any statement about permissions, side effects, or whether the check is purely read-only.
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 dense sentences, with the core purpose front-loaded and the matching semantics immediately after. The telegraphic list 'model, codename, revision, SoC or firmware identifiers' is efficient but slightly compressed.
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?
An output schema exists, so return values needn't be spelled out, and the description still usefully explains the three outcome states. For a two-parameter, nested-object check tool this is nearly complete; only the source/definition of the 'curated target' and any permission constraints are 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?
Reported top-level schema coverage is 0%, so the description must compensate; it names the salient observed fields (model, codename, revision, SoC, firmware) and the exact-match semantics, but leaves playbook_id unexplained beyond 'one firmware playbook' and omits the sensor/wifi_module fields. The nested field descriptions in the schema do most of the remaining work.
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 ('Fail-closed compatibility check for one firmware playbook'), which cleanly separates it from siblings like find_firmware_playbooks and get_firmware_playbook that retrieve rather than evaluate. It doesn't explicitly name an alternative tool, so it falls just short of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the purpose – call it when you need to validate observed hardware facts against a curated playbook target. However, there is no explicit when-to-use/when-not guidance, no prerequisites (e.g. playbook must already be fetched), and no routing to sibling tools like verify_firmware_artifact.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find curated owner-authorized firmware, local-protocol and cloud-replacement playbooks. Results prefer the least-invasive path and are not compatibility approval; call check_firmware_compatibility before preparation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| device_id | No | ||
| intervention | No | ||
| risk_tolerance | No | moderate |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral load, and it does disclose two meaningful traits: results are ranked toward the least-invasive path, and the output is explicitly not a compatibility approval. It omits any note on authorization requirements beyond 'owner-authorized' or on result volume, but the disclosed caveats are operationally valuable.
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 what the tool returns and immediately followed by the actionable caveat and prerequisite. Every clause earns its place with no padding.
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?
An output schema exists, so return values need not be explained, and the purpose plus prerequisite are covered. However, for a 5-parameter search tool with 0% schema coverage, the absence of any parameter guidance leaves the agent guessing how to steer results.
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% across 5 parameters, and the description adds no meaning for query, device_id, limit, intervention, or risk_tolerance. The two enums are self-describing in the schema, but the description does nothing to explain how risk_tolerance or intervention shape which playbooks are returned, which is exactly the gap the description should fill.
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?
It states a specific verb (find) and resource (curated owner-authorized firmware/local-protocol/cloud-replacement playbooks), which distinguishes it from the singular get_firmware_playbook. It does not explicitly contrast itself with that sibling or with assess_hackability, so an agent must infer the difference from the plural-collection framing.
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 a clear prerequisite chain: call check_firmware_compatibility before preparation, and results are explicitly 'not compatibility approval.' That routes the agent to the right sibling at the right step. It stops short of saying when to prefer this over get_firmware_playbook for retrieving a specific playbook.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Hackshop's curated hardware reverse-engineering examples and research methods. With no query, returns the newest dated examples first. Evidence labels distinguish reproduced work, documented implementations, author demos and analysis methods; they do not imply compatibility with similar-looking hardware.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | Optional case-insensitive search across title, device, exact models, summary, outcome and tags. Empty returns the latest examples. | |
| method | No | ||
| category | No | ||
| evidence | No | ||
| use_case | No | Filter source-backed design starters by intended use. | |
| max_device_price_usd | No | Maximum lowest-listed device price in USD; unknown prices excluded. Accessories, host, shipping and tax are extra; availability is returned separately. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose meaningful behavior: default ordering (newest dated examples first) and the semantics of evidence labels, including the important caveat that they do not imply compatibility. It omits auth requirements, pagination limits, and result-cap behavior, so it is solid but not complete.
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 ordering, then an important semantic caveat. No filler, though the evidence-label sentence is dense and 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?
An output schema exists, so return values need no explanation, and the description covers ordering plus evidence semantics. However, with no annotations and seven parameters, it leaves the safety/permission profile and several filter enums unspecified, so it is only adequate for a tool of this 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 coverage is 43%, below the 50% threshold, so the description should compensate more than it does. It clarifies the empty-query behavior and evidence-label meaning, but the method and category enums remain undocumented in both schema and description, leaving notable gaps.
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 (Search) and resource (Hackshop's curated hardware reverse-engineering examples and research methods), which is unambiguous. It does not compare itself to siblings like find_firmware_playbooks, so the agent must infer the distinction, capping it below 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: it hints that an empty query returns the newest examples, but never states when to choose this tool over the firmware-playbook or hackability siblings. No explicit when/when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the complete deterministic build plan for a catalog device id: difficulty, board-specific flash warnings, parts, shopping list with buy_options, prices checked date and purchase policy, steps, machine-readable assembly with verify checks, commands, links, caveats and an agent-ready brief. Input: device_id 1-200 chars.
| Name | Required | Description | Default |
|---|---|---|---|
| device_id | Yes | Catalog device id, 1-200 characters. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 strongly conveys the deterministic, read-oriented nature of the result and lists what the plan contains, but says nothing about permissions, rate limits, caching, or failure modes for an invalid/unknown device id.
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 purpose, and the second sentence is a compact input spec. The middle enumeration of plan contents is a long run-on list, but each item does convey real scope rather than 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?
With an output schema present, the description need not detail return values, yet it usefully summarizes the plan's sections. For a single-parameter read tool, the definition is essentially complete; only routing guidance against siblings is absent.
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 the single device_id parameter, so the schema already documents type and length constraints. The description merely restates 'Input: device_id 1-200 chars', adding no meaning beyond the structured field.
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 ('Return the complete deterministic build plan') and enumerates the plan's contents, so the agent knows exactly what it gets back. It does not, however, differentiate itself from siblings like plan_gadget or assess_hackability, which is the remaining gap.
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 call this versus plan_gadget or the other siblings, and no prerequisites or exclusions are stated. The agent must infer that a catalog device_id is the trigger condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a curated firmware playbook with exact targets, prerequisites, backups, recovery, independent risk axes, sources, validation checks and the human-only destructive-action boundary.
| Name | Required | Description | Default |
|---|---|---|---|
| playbook_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The word 'Return' strongly implies a read-only operation, and the description adds useful context about the playbook containing a 'human-only destructive-action boundary.' However, it does not explicitly state read-only status, side effects, permissions, or rate 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?
The description is a single, front-loaded sentence with no filler. The long comma-separated list of contents is dense but each item earns its place by specifying what the playbook includes.
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?
An output schema exists, so the description need not explain return values, yet it does provide a detailed contents list. The main gaps are the absence of usage guidance relative to the sibling find_firmware_playbooks and no semantic detail for the required playbook_id parameter.
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 single playbook_id parameter has zero schema description coverage, and the description does not explain the parameter at all. While the parameter name is self-explanatory, the description adds no meaning beyond what the name already conveys, failing to compensate for the coverage gap.
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 verb ('Return') and resource ('curated firmware playbook'), and it enumerates the playbook's contents. However, it does not explicitly distinguish this tool from the sibling find_firmware_playbooks, so an agent must infer the singular-by-id retrieval role from the required playbook_id parameter.
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 explicit when-to-use guidance, no prerequisites, and no mention of alternatives such as find_firmware_playbooks. The description only implies that this tool is used to retrieve a playbook, leaving routing decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the 4 intake questions (where it lives, how you interact, room sensing, budget), why each matters and how each answer maps to plan_gadget. Ask the human these, then call plan_gadget with answers. No input.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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. 'Return' and 'No input' imply a read-only, zero-argument retrieval, and it states the returned content. It omits explicit auth, rate-limit, or side-effect details, but those are minimal for this simple read 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?
Two compact sentences front-load what is returned and then the required follow-up workflow. Every clause adds useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so the description need not explain return formatting. It supplies the purpose, expected content, workflow, and no-input constraint, which is complete for this retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, so the baseline is 4. The description's 'No input' matches the empty input schema and leaves no ambiguity about arguments.
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: 'Return the 4 intake questions...' and enumerates the four categories plus why each matters and how answers map to plan_gadget. It distinguishes itself from siblings by explicitly positioning plan_gadget as the next step.
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 clear workflow guidance: 'Ask the human these, then call plan_gadget with `answers`. No input.' This tells the agent when to use the tool in a sequence, but it does not state when not to use it or compare against siblings like get_build_plan or assess_hackability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Deterministically plan a physical body for an AI agent using Meta Muse boards. Ask the intake questions first (intake_gadget, or the questions this returns until intake.complete is true), then pass the human's choices as answers. Inputs: idea 3-2000 chars (optional when answers or needs are given); answers {size, interaction, sensing, budget} using the intake option values; platform one of muse-esp32, muse-linux, any (default any); budget_usd number greater than 0, up to 100000; owned_device_ids up to 50 catalog ids; needs values: voice, screen, images, touch, camera, air-sensors, e-ink, battery, round, home-tunnel, big-screen, compute, linux; size one of pocket, desk, wall, hidden, any (default any); limit 1-5 (default 3). Boards that meet the hard needs and the budget rank first, cheapest first. Returns fit, notes, warnings, intake status, questions, picks with difficulty, terms and next steps.
| Name | Required | Description | Default |
|---|---|---|---|
| idea | No | Gadget idea or use case, 3-2000 characters. Optional when `answers` or `needs` are given. | |
| size | No | Preferred size or placement: pocket, desk, wall, hidden, any. Default any. | any |
| limit | No | Number of picks to return: 1-5, default 3. | |
| needs | No | Optional explicit needs. Allowed: voice, screen, images, touch, camera, air-sensors, e-ink, battery, round, home-tunnel, big-screen, compute, linux. | |
| answers | No | The human's intake answers, by question id, using the option values from intake_gadget. | |
| platform | No | Platform filter: muse-esp32, muse-linux, or any. Default any. | any |
| budget_usd | No | Optional hardware budget in USD. Must be greater than 0, max 100000. | |
| owned_device_ids | No | Catalog device ids the user already owns. Max 50. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does disclose ranking behavior ('Boards that meet the hard needs and the budget rank first, cheapest first') and the ranked output categories. It omits permission/auth needs, whether planning is idempotent, and any rate limits, but the determinism and ranking claims are genuinely useful behavioral context.
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 purpose and workflow lead are front-loaded and useful, but the bulk of the text is a run-on re-enumeration of values (needs list, sizes, platforms) already fully specified in the schema, which bloats the definition without adding decision value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an 8-parameter tool with a nested `answers` object, an output schema, and no annotations, the description covers prerequisites, cross-parameter optionality, ranking logic, and a summary of returns. It is close to complete, with only auth/permission context 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 coverage is 100%, so the schema already documents every parameter; the description nonetheless adds cross-parameter meaning, notably that `idea` is optional when `answers` or `needs` are given and that `answers` must use intake_gadget option values. That cross-tool linkage justifies going above the 3 baseline.
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 ('plan a physical body ... using Meta Muse boards') and even characterizes the algorithm as deterministic. It distinguishes itself from intake_gadget by positioning that tool as a prerequisite, but leaves its relationship to get_build_plan and assess_hackability unaddressed.
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 sequencing rule: ask the intake questions first (via intake_gadget or the returned `questions` until `intake.complete` is true), then pass the human's choices as `answers`. That is actionable context, though it offers no explicit when-not-to-use or comparison against the other planning siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prepare a non-executing firmware manifest after owner authorization and an exact compatibility match. Returns backup, recovery, sources, validation and stop points; never returns a flash command.
| Name | Required | Description | Default |
|---|---|---|---|
| observed | Yes | ||
| playbook_id | Yes | ||
| owner_authorized | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and does well: it declares the operation is non-executing, requires authorization plus exact compatibility, and explicitly guarantees no flash command is ever returned. It omits failure modes (e.g. what happens on a compatibility mismatch) and any rate/permission detail, keeping it below 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?
Two tight sentences, zero filler, and the safety-critical qualifier 'non-executing' is front-loaded before the return-value sentence. 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?
An output schema exists, so the return-value sentence is partly redundant, but it usefully adds the negative guarantee of no flash command. Combined with the preconditions stated, an agent has enough to invoke it correctly; only playbook_id sourcing and mismatch behavior 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?
Top-level schema coverage is 0%: playbook_id and owner_authorized have no descriptions. The description partially compensates by tying owner_authorized to an authorization requirement and implying 'observed' must yield an exact compatibility match, but playbook_id's provenance is left unexplained. The rich nested 'observed' descriptions live in the schema, not the description.
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 ('Prepare a ... firmware manifest') and adds the crucial qualifier 'non-executing', which tells an agent this is a planning/assembly step rather than a flash operation. It is reasonably distinguishable from siblings like check_firmware_compatibility or get_firmware_playbook, though it never names an alternative directly.
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 two concrete preconditions for use: owner authorization and an exact compatibility match, implying the agent should run check_firmware_compatibility first and only proceed when authorized. It stops short of naming alternative tools or stating when not to use it, so it is clear context without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare a caller-supplied filename, source URL and SHA-256 with Hackshop's curated metadata. Hackshop does not fetch, upload or redistribute the artifact; absent hashes return unknown.
| Name | Required | Description | Default |
|---|---|---|---|
| sha256 | Yes | ||
| filename | Yes | ||
| source_url | Yes | ||
| playbook_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It usefully discloses that the tool performs no fetch/upload/redistribution (a pure comparison, side-effect free) and that absent hashes return 'unknown', which is real behavioral context. However, it omits permissions/auth needs, rate limits, and what a mismatch or unknown result implies for the caller.
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 the comparison action front-loaded and the side-effect disclaimer and unknown-result behavior following. No filler, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return-value detail is not required, and the description's note about unknown hashes is a bonus. Still, playbook_id goes unexplained, and there is no guidance on what the caller should do with a match versus a mismatch, leaving a gap for a four-required-parameter verification 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 names three of the four parameters (filename, source_url, sha256) and clarifies they are caller-supplied and compared against curated metadata, but playbook_id is never mentioned anywhere, leaving one required parameter with no semantic explanation at all.
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: comparing caller-supplied filename, source URL and SHA-256 against Hackshop's curated metadata. This is clearly a verification/lookup tool and is distinguishable from siblings like prepare_firmware_job or check_firmware_compatibility, though the description never names an alternative to sharpen the boundary.
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 (verify a firmware artifact's provenance before relying on it) but there is no explicit when-to-use, when-not-to-use, or sibling routing. The single constraint given, that Hackshop does not fetch or redistribute the artifact, narrows scope somewhat but is framed as behavior, not as a usage rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
find_reverse_engineering_examplescheck_firmware_compatibilityfind_firmware_playbooksget_firmware_playbookprepare_firmware_jobverify_firmware_artifactintake_gadgetplan_gadget3 fields changedInput schema / properties / answersAdded value: +{
+ "additionalProperties": false,
+ "description": "The human's intake answers, by question id, using the option values from intake_gadget.",
+ "properties": {
+ "budget": {
+ "description": "What budget should I plan around? under-25 = Under $25; under-50 = Under $50; under-100 = Under $100; no-limit = No limit.",
+ "enum": [
+ "under-25",
+ "under-50",
+ "under-100",
+ "no-limit"
+ ],
+ "type": "string"
+ },
+ "interaction": {
+ "description": "How should you interact with it? voice = Talk to it; touch = Touch screen; light-button = Light and button.",
+ "enum": [
+ "voice",
+ "touch",
+ "light-button"
+ ],
+ "type": "string"
+ },
+ "sensing": {
+ "description": "Should it sense the room? camera = Camera; air-quality = Air quality; none = None.",
+ "enum": [
+ "camera",
+ "air-quality",
+ "none"
+ ],
+ "type": "string"
+ },
+ "size": {
+ "description": "Where should this body live? pocket = Pocket; desk = Desk; wall = Wall or fridge; hidden = Hidden, no screen.",
+ "enum": [
+ "pocket",
+ "desk",
+ "wall",
+ "hidden"
+ ],
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Input schema / properties / idea / descriptionPrevious value: -"Gadget idea or use case, 3-2000 characters."New value: +"Gadget idea or use case, 3-2000 characters. Optional when `answers` or `needs` are given."Input schema / requiredRemoved value: -[
- "idea"
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