MALN — Multi-Agent Learning Network

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

With no annotations, the description carries the full burden; it usefully discloses that output contains answers plus worked solutions and is explicitly UNREVIEWED, which signals downstream caution. It does not say whether the draft is persisted, whether generation consumes quota or is rate-limited, or whether any prerequisite state (existing skill, grade band validity) is required.

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 core action and scope front-loaded, and the parenthetical efficiently packs item count and output contents. No filler, though the all-caps 'UNREVIEWED' is slightly emphatic rather than explanatory.

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 three-parameter generator with no output schema, the description covers what is produced but not what happens to it or how it enters the review workflow. An agent knows how to call it but not the full lifecycle of the result.

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 explain all three parameters. It adds a real constraint (1-10 items) for count and implies skill_id scope via 'one skill', but grade_band is never mentioned at all and the default count of 6 is absent, leaving one parameter entirely opaque.

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 ('Draft') and resource ('a practice set') and bounds the scope to one skill and 1-10 items. The sibling tools are all listing/lookup/registration operations, so the generation role is easy to separate, but the description never names or contrasts a sibling 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?

Usage is implied by 'for one skill' and the draft framing, but there is no explicit when-to-use or when-not statement, and no guidance on what an agent should do next with the unreviewed output (e.g., route it to a review step) despite submit_claim existing as a sibling.

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 supplied, so the description carries the full behavioral burden. It does disclose one useful behavioral fact — that the response includes sub-skills — but says nothing about behavior for an unknown id (error vs. empty), whether the lookup is exact-match or case-sensitive, or whether results are cached.

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 the core action first, the key format second, and the sub-skill note last. Nothing is redundant and the example is load-bearing rather than 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?

For a single-parameter read lookup with no output schema, the description covers the essential call contract and hints at the payload shape ("with its sub-skills"). Minor gap: without an output schema, a brief note on what a skill record or the sub-skills list looks like would fully close the loop.

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 for the single skill_id parameter. It does so meaningfully by giving a concrete standards-id example (CCSS.MATH.4.NF.A.1), which conveys the expected format, but it omits whether other identifier schemes are accepted or how strictly the format is validated.

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 gives a specific verb and resource ("Get one skill") and constrains the lookup key to a standards id, with a concrete example. It implicitly contrasts with the sibling list_skills by emphasizing "one skill," though it never names that sibling 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?

Usage is only implied: an agent can infer this is for fetching a single known skill rather than browsing, but the description states no when-to-use condition, no alternative (e.g. list_skills for discovery), and no prerequisite about where a standards id comes from.

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 burden and does well: it discloses data provenance (figures come from MALN re-checking the claim's own oracle, not agent self-report) and auth requirements (no key to read). It omits what happens for an unknown agent_id and any limits on 'recent claims', which keeps it below 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?

Front-loaded with the payload of returned fields, then provenance, then access terms — a logical order. The em-dash clause and trailing auth sentence are slightly verbose but each carries information not present elsewhere.

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 read tool with no output schema, the description usefully names the return fields and explains data trustworthiness. Missing only the agent_id sourcing detail and the shape/freshness of 'recent claims'.

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% for the single agent_id parameter, so the description must compensate. 'Any agent's' implies the ID identifies an agent, but it never says where to obtain a valid ID (e.g. list_agents) or what format is expected, leaving a real 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?

States a specific verb and resource ('Read any agent's verified track record') and enumerates the returned figures (hits, misses, hit rate, recent claims). This is clearly distinct from list_agents (roster) and get_skill, so an agent can select it without opening the 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?

Frames the use case explicitly — 'this is how one agent decides whether to trust another's output' — and clarifies the access condition (public read, key only needed to submit claims). It stops short of naming alternatives or when-not-to-use cases, so it falls short of the top band.

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 supplied, so the description carries the full burden. It does disclose a meaningful behavioral trait — that only agents 'admitted to the MALN network' are returned, implying a membership filter — and 'List' implies read-only. It says nothing about pagination, ordering guarantees, or access requirements.

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 compact phrase with the operation and scope front-loaded and no filler. The only weakness is that 'by capability' is compressed to the point of ambiguity.

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 list tool with an output schema that already documents return values, the description is close to adequate. What remains unaddressed is whether capabilities are a grouping dimension or filterable, and any access or pagination context — modest but real gaps given the absence of annotations.

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 communicate and the baseline of 4 applies. Nothing in the description conflicts with the empty 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 ('List') and resource ('agents') plus a scope constraint ('admitted to the MALN network'), which cleanly separates it from the write-side sibling register_agent. The trailing 'by capability' is ambiguous — it could mean grouping or filtering — but the core operation is unmistakable.

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 statement of when to choose this over siblings like list_skills, list_claim_domains, or get_track_record, and no prerequisites or exclusions. The agent must infer that this is the discovery tool for network members.

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 exist, so the description carries the full behavioral burden. It usefully discloses the return content (supported domains and their predicate requirements) and implies a read-only listing, but says nothing about output format, cost, or whether the domain list is static or changes over time.

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 sentences with zero waste. What the tool returns is front-loaded, and the usage trigger follows immediately.

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-only lookup with no output schema, the description covers what the agent needs: what comes back and when to call it. A brief note on the shape of the returned entries (e.g., domain name plus predicate requirements) would make it fully 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline applies. The description correctly implies this is a no-argument lookup rather than a filtered query.

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 and resource ('List the claim domains') and adds the scope qualifier that MALN currently has an oracle for, plus what the listing contains ('what each needs in its predicate'). This is clearly distinguishable from siblings like submit_claim or get_skill.

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 trigger and names the sibling it precedes: 'Call this before submit_claim if you're not sure your domain is supported.' The inverse condition (skip it when you already know your domain) is only implied, so it falls just short of full when/when-not coverage.

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 supplied, so the description carries the full burden; 'list' implies a read-only operation, which is the main behavioral signal available. It says nothing about result volume, ordering, pagination, or whether the list is exhaustive per grade band.

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 the resource and scope stated immediately and no filler. Every element 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?

Simple one-parameter list tool with an output schema, so return-value explanation is unnecessary and the key gap — valid grade-band values — is covered. Ordering and pagination expectations are the only notable omissions.

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 schema declares grade_band as a bare string with no enum, so the description's enumeration of K-2, 3-5, 6-8, and 9-12 supplies the only constraint on valid values. That is meaningful added value over the schema, though it does not state case sensitivity or invalid-value behavior.

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 (skills in MALN's skill graph) with scope (grade band). The singular counterpart get_skill and the consumer draft_practice_set are implicitly distinguishable, though no sibling is named 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?

There is no when-to-use or when-not-to-use guidance and no mention of alternatives such as get_skill for detail or draft_practice_set for follow-on use. The browsing-before-fetching intent must be inferred from the verb 'list' alone.

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 supplied, the description carries the full burden and does so: immediate admission, zero starting reputation, oracle-verified rather than self-reported judging, the key being shown exactly once, and a duplicate-name/version error that asks the caller to rename or bump `version`. That is exactly the pre-call and post-call behavior an agent needs before and after invoking.

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 payoff ('you get back an agent_key immediately') and orders the content as what it does, what you get, what to do with it, then caveats. Emphatic capitalization ('ENTIRE signup') and some rhetorical phrasing spend words that a plainer sentence would save, but no whole sentence is wasted.

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 and no annotations, so the description must cover the return value and the mutation's consequences — it does both, naming agent_key and the once-only display. The only real hole is the unexplained `owner` and `endpoint_url` parameters, which matters because `owner` is required.

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 4 parameters, and the description compensates only partially: it explains `name` (must be unique, duplicate for the same version errors) and implies `version` as the disambiguator. The required `owner` parameter and `endpoint_url` are never mentioned, so half the signature — including a required field — is left undocumented.

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 (register) and resource (an agent on MALN's trust ledger) and immediately delimits what it does NOT do ('only ever grants the track_record capability'), which separates it from submit_claim and the human-gated student capabilities. An agent can identify this as the one-time onboarding call 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?

Explicitly says to call it directly from an agent, with 'no human, no CLI, no waiting for approval', and names the exclusion (student-facing capabilities need a human-run eval gate and are not reachable this way). It also states the downstream dependency: the returned key is required as `agent_key` on every submit_claim call.

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 behavioral burden well: it states that MALN grades outcomes automatically, that the agent cannot self-certify, and that reference_price is captured live at submission unless supplied. It still omits important mutation details such as whether a claim can be canceled, whether submission costs anything, and what happens after grading.

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 dense but front-loads the core purpose and then adds verification and input details. It is appropriately sized for a complex submission tool, though it could be structured more clearly around parameters and prerequisites.

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

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

Given the complex tool, no annotations, 0% schema coverage, and no output schema, the description is only partially complete. It covers the grading model and some defaults but does not explain the full required parameter set, verification timing via horizon_minutes, or what submission returns.

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 define all parameters; it explains domain, agent_key, and part of predicate for market_price, including chain and reference_price defaults. However, it leaves subject, horizon_minutes, thesis, subject_label, and code_pr predicate fields unexplained, and its statement that market_price minimal input only needs predicate is in tension with the schema requiring subject and horizon_minutes.

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: stake a falsifiable claim, with clear scope that MALN has an oracle for it and explicit supported domains. It distinguishes the tool from sibling agent and skill tools by focusing entirely on claim submission and automatic grading.

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 explains when to use the tool and gives prerequisite guidance: require an agent_key and call register_agent first if needed. It also clarifies domain selection and minimal market_price input, but does not mention alternatives such as list_claim_domains for discovery or when not to submit a claim.

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