63-layer continuous-time Symplectic Neural ODE computing topological invariants of real market signals. 256-dim feature space. 27 data products with autonomous Stripe purchase. Property intelligence, DAG manifold computation, federal contracts, distressed real estate. 500K validated embeddings.
The tools mostly target distinct actions, but the proof/verification cluster (get_derivation_ledger, get_zk_provenance, verify_footprint) and the metadata cluster (list_datasets, get_data_dictionary) have overlapping purposes that could cause misselection. Descriptions help clarify boundaries, but get_stats is notably vague and could be confused with fetch_dataset.
All tool names follow a consistent lower_snake_case verb_noun pattern: check_balance, fetch_dataset, get_zk_provenance, list_datasets, purchase_data, verify_footprint. Verb choice varies appropriately by action, and there are no mixed casing conventions or cryptic names.
Twelve tools is well-scoped for a data marketplace server covering account management, data discovery, data retrieval, and provenance verification. Each tool appears to serve a distinct functional need without unnecessary bloat or duplication.
The core lifecycle is covered: registration, top-up, balance check, product listing, purchase, dataset listing, data fetching, and multiple verification paths. Minor gaps exist, such as no way to list current purchases/entitlements or cancel/manage subscriptions, but agents can complete primary workflows without dead ends.
Check your remaining credit balance. Use after purchases to see remaining credits.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | Your API key |
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 only states the action and timing but does not disclose whether the operation is read-only, potential side effects, error handling, or the return format. This is a gap for a tool with no annotation support.
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 two short sentences, front-loaded with the primary purpose and a practical usage tip. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema and no annotations, the description adequately states what it does and when to use it. However, it omits details about the response format or error conditions, which would make it more complete.
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 api_key parameter, with a clear description 'Your API key'. The tool description adds no parameter-specific meaning, but the baseline of 3 applies given full schema 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 clearly states the resource ('remaining credit balance') and specific verb ('check'). It distinguishes from sibling tools like purchase_data and topup_credits by explicitly focusing on checking the balance after purchases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use: 'Use after purchases to see remaining credits.' It doesn't explicitly state exclusions or alternatives, but the context is sufficient for this simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query records from a dataset with limit/offset. Free tier: 10 records per query. For depin_onchain, raw archived observations, including rejected rows, are retained. observation_quality reports limited PKT airdrop checks; not_checked means not validated. This read-time annotation is not covered by original proof hashes.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max records (max 10 for free tier) | |
| offset | No | Record offset | |
| dataset | Yes | Dataset name (e.g., depin_onchain) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It goes well beyond the schema by revealing the free-tier query limit, the retention of rejected rows for depin_onchain, the meaning of not_checked in observation_quality, and the important trust caveat that this read-time annotation is not covered by original proof hashes. This gives an agent critical context about data quality and provenance before invocation.
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 compact and well-structured: the core action is front-loaded in the first sentence, and subsequent sentences deliver essential operational and data-quality caveats. Every sentence earns its place; there is no filler or unnecessary repetition.
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 absence of annotations and output schema, the description is substantially complete: it explains the query behavior, the pagination limits, and non-obvious data-quality caveats. It does not describe the exact shape of returned records or error behavior, but these are secondary for a dataset fetch operation and the provided context is sufficient for safe and correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents all three parameters with descriptions, including the free-tier max for limit, so the baseline is 3. The description adds a bit of context by tying limit to the free tier and giving depin_onchain as a dataset example, but it does not materially redefine or expand the parameter semantics beyond what the schema already states.
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 opens with a specific verb and resource: 'Query records from a dataset with limit/offset.' It clearly identifies the tool as a record-fetching operation and distinguishes it from sibling tools like list_datasets or get_data_dictionary, which handle metadata or dataset discovery rather than record retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context: use this tool to query dataset records with pagination controls. It also gives a concrete operational constraint via the free-tier limit ('10 records per query'). It does not explicitly name alternatives or exclusions, but no sibling tool competes directly with fetching dataset records, so the intended usage is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ONBOARDING SPEC: freshness, history depth, coverage and endpoints per feed (depin_onchain, depin_daily, health_profiles, signal_ledger, market_ticks, zk_footprints). Machine-legible; answers 'how stale is this and how far back does it go' for every feed. Free, no key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly discloses 'Free, no key' and 'Machine-legible', which are useful behavioral notes. However, it does not mention response structure, caching, or potential latency, so it is not fully transparent but above average.
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, dense sentence that front-loads the purpose with 'ONBOARDING SPEC' and efficiently lists the key metadata dimensions and feeds. It avoids extraneous phrasing and earns every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no parameters, the description covers the content types but not the exact structure of the response (e.g., whether it is a mapping of feed to metadata, keys, or value formats). 'Machine-legible' hints at structure but does not fully specify it. An agent may need additional context to parse the output correctly.
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 zero parameters, so the schema is trivially complete. Per rubric, 0 parameters warrants a baseline of 4. The description adds no parameter-specific meaning because none exist, so the baseline holds.
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 clearly states the tool provides an onboarding spec with freshness, history depth, coverage, and endpoints for six named feeds. It answers a specific question about staleness and historical depth, and its unique metadata purpose distinguishes it from sibling tools like list_datasets or fetch_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used when an agent needs freshness or history metadata for a feed, but it does not explicitly state when to use it versus alternatives, nor does it mention when not to use it. It lacks direct comparison to siblings or exclusions, leaving some inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
DERIVATION TRUTH: raw upstream payloads, SHA-256-pinned, with the exact collector code hash for each fetch. Replay any published value: fetch the verify_url yourself, run the pinned code, compare. Answers 'a hash proves a footprint, not the derivation' — divergence from origin is mechanically detectable. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Optional: filter to one symbol (e.g. AKT). Omit for the coverage list. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral disclosure burden. It adds useful context: data is SHA-256-pinned, each fetch includes a collector code hash and verify_url, and it is free. However, it omits read-only confirmation, response shape, pagination/volume expectations, and any auth or rate-limit notes.
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 short, but the all-caps branding and aphoristic phrasing reduce clarity. The actionable details—pinned payloads, code hash, verify_url, free—are present, yet buried under philosophical framing rather than front-loaded as a direct tool description.
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 and no output schema, this is a low-complexity case. The description names the core output concepts and a replay procedure, but leaves the response structure and the exact meaning or format of the 'coverage list' 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?
The schema already documents the sole parameter, symbol, with 100% coverage, so the baseline is 3. The description adds no additional meaning about the parameter or the coverage list, but given the schema coverage that is not a significant 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 identifies a distinct resource—raw upstream payloads with SHA-256 pins and collector code hashes—but never states the operation in a plain main clause such as 'returns' or 'lists'. It hints at divergence verification but does not clearly differentiate this from siblings like verify_footprint or get_zk_provenance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use guidance or alternative routing is provided. 'Replay any published value' is an imperative for the caller, and the 'answers...' line suggests a use case, but an agent is not told when to choose this over verify_footprint or get_zk_provenance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get aggregate statistics from the databases
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not mention whether the operation is read-only, what statistics are computed, or any side effects or limitations, leaving the behavior largely unspecified.
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 focused sentence with no wasted words. However, it is vague enough that a fuller explanation would improve its value while still remaining concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no parameters, no output schema, and no annotations, so the description must provide sufficient context on its own. It only says 'aggregate statistics from the databases,' leaving unclear what statistics are available, which databases are involved, or how results are returned.
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 zero parameters, so the baseline is 4. The description correctly implies no inputs are needed, and there is no additional parameter information required.
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 action ('Get') and resource ('aggregate statistics from the databases'), making the tool's purpose clear. It is distinct from sibling tools like list_datasets or check_balance, though it does not explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description simply states what it does without providing context for appropriate usage or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return cryptographic provenance proof for a record ID from current datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| record_id | Yes | Record ID to get provenance for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool generates a cryptographic proof, implying a read operation, and scopes it to current datasets. However, it does not describe error behavior, whether any authentication is implicitly required, or the format of the returned proof. Since no annotations are present, the description carries the full burden and provides only basic information.
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 sentence that is concise and front-loaded with the primary action and object. It contains no redundant information, achieving high efficiency.
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?
The tool is simple with one parameter and no output schema, so the description's one-sentence definition is partially adequate. However, without annotations or output schema, the description does not help the agent understand response structure or failure modes, leaving some gaps for a complete mental model.
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 schema description for record_id is straightforward and covers 100% of the parameter, so the tool description adds no additional semantic value. The description in the tool text does not elaborate on the record_id format or constraints beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'return' and identifies the resource as 'cryptographic provenance proof for a record ID', clearly distinguishing it from sibling tools like verify_footprint. The scope 'from current datasets' adds useful context. No sibling tool mentions provenance, so this uniquely identifies the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as verify_footprint or fetch_dataset. The description does not mention any prerequisites, exclusions, or typical use cases. This is a significant gap given the related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available datasets with record counts. Free to browse — market ticks, DePIN network stats, technical indicators, US county atlas, ZK footprints.
| Name | Required | Description | Default |
|---|---|---|---|
No 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 discloses that browsing is free and that record counts are included, but doesn't mention output structure, pagination, or any potential side effects. For a zero-parameter list tool, this is adequate but minimal.
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 the core purpose. The second sentence adds relevant examples without redundancy. Every word 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 simple list tool with no parameters and no output schema, the description is reasonably complete. It includes dataset categories and cost indication. It could mention return format or next steps, but those aren't essential for this straightforward 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 input schema is empty, so schema coverage is 100% with zero parameters. The description correctly omits parameter details since none exist. Baseline 4 is appropriate.
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 'List all available datasets with record counts' — a specific verb, resource, and output detail. It clearly distinguishes from sibling tools like 'fetch_dataset' (which fetches a specific dataset) and 'list_products' (which lists products, not datasets).
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 'Free to browse' gives clear context for when to use the tool (browsing datasets without cost). The listed data categories (market ticks, DePIN stats, etc.) imply exploration use cases. It doesn't explicitly state exclusions or compare to alternatives, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Browse all purchasable products with prices in credits ($1 = 1 credit). Includes DePIN supply-telemetry data, signal feed tiers ($9.99-$399.90/mo), DAG manifold API ($2.99-$9.99/mo), GPU compute, and MCP unlimited access. Call after register_agent to see what to buy.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It indicates a read-only action via 'Browse' and includes pricing/credit context, but it does not disclose output format, pagination, authorization requirements, or any behavior if called before register_agent. For a tool with no annotations and no output schema, this is insufficient.
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 concise sentences. The first sentence front-loads the purpose, the second gives concrete examples with prices, and the third offers actionable guidance. Every sentence earns its place and there is 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?
The description covers the essential product scope and pricing, and gives usage context. Yet, without an output schema, it does not describe the response structure, field names, or pagination, which would be helpful for a list tool. Still, the provided content is largely sufficient for an agent to understand what to expect.
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 zero parameters and the schema is empty, so the baseline according to the rubric is 4. The description adds context about product categories and pricing, but there is no parameter-specific information to clarify. This is appropriate for a 0-parameter tool.
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 clearly states the tool's purpose: 'Browse all purchasable products with prices in credits.' It lists specific product categories and price ranges, and explicitly distinguishes itself from sibling tools like register_agent by saying 'Call after register_agent to see what to buy.' This is a specific verb (browse) + resource (purchasable products) with clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Call after register_agent to see what to buy,' which tells the agent when to use this tool. However, it does not mention alternatives or when not to use it, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Buy a product with your credits. Products: DePIN supply-telemetry snapshots, DAG manifold API (dag_pro/dag_quant/dag_enterprise), signal feed tiers, GPU compute, MCP unlimited. Use api_key from register_agent.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | Your API key from register_agent | |
| product_key | Yes | Product key to purchase (e.g. dag_pro, mcp_unlimited) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It does disclose that the purchase consumes credits and requires an API key, and it enumerates the products available. However, it does not mention outcomes (e.g., what is returned on success/failure), reversibility, or credit insufficiency handling, leaving some ambiguity.
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 two sentences long, highly efficient, and front-loaded with the purpose ('Buy a product with your credits'). The product list is compact and informative, with no redundant phrases.
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?
The tool is simple with two parameters and no output schema. The description covers the purpose, products, and a prerequisite (api_key). It lacks details about the response format or post-purchase behavior, but given the simplicity and sibling context, it is largely 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 schema covers both parameters with descriptions, and the tool description adds specificity by naming the API key source (register_agent) and providing product examples (dag_pro, mcp_unlimited). This goes beyond the schema's generic 'Product key to purchase' description, so the added value warrants above 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?
The description clearly states a specific verb ('Buy'), the resource ('product'), and the payment method ('credits'). It also enumerates the specific product catalog, which distinguishes it from sibling tools like topup_credits (adds credits) and register_agent (creates API key).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool to spend credits on listed products, and it references the prerequisite API key from register_agent. It doesn't explicitly contrast with alternatives, but the context and sibling list make the intended usage unambiguous. It could be improved by stating when not to use it (e.g., before topup_credits).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
START HERE. One call: get $5 free credits + API key, instantly (no card, no human, no Stripe, ~5 seconds). Works immediately: query live DePIN telemetry (457 symbols, every value carries a verify_url you can check yourself), GPU inference, DAG manifold. Try get_data_dictionary first if you want the coverage spec, or GET https://kairossignal.com/try with zero setup. Nothing to cancel; credits just sit there until you spend them. Then list_products to see what $0.49+ buys.
| Name | Required | Description | Default |
|---|---|---|---|
| No | OPTIONAL contact email (for delivery and topup notifications). Omit to register anonymously. | ||
| agent_name | Yes | Your agent name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses notable behavioral traits: no card or human required, instant (~5 seconds), no Stripe involvement, nothing to cancel, and credits that sit until spent. It stops short of describing the exact response format or idempotency, but it provides substantial 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 description is longer than strictly necessary, with some promotional phrasing, but it front-loads the purpose ('START HERE. One call...'), then adds value with alternatives, next steps, and behavioral reassurances. Each sentence contributes useful orientation even if some could be trimmed.
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 two-parameter registration tool with no output schema, the description covers what you get (free credits, API key), that it works immediately, and what to do next. The precise response format is not described, but the essential usage context and integration with sibling tools are present.
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 the baseline is 3. The tool description itself adds no parameter-specific meaning beyond the schema's own descriptions (email optionality is already documented in the schema). It neither subtracts nor adds significant value here.
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 clearly states the action: 'START HERE. One call: get $5 free credits + API key, instantly' — a specific verb (get/register) with a distinct resource (credits + API key). It also differentiates itself from siblings by positioning as the entry point and directing users to list_products afterward.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'START HERE', establishing when to use this tool as the first step. It offers an alternative (get_data_dictionary for coverage spec, or a URL for zero-setup access) and suggests list_products as the next step. It could more explicitly state when not to use it, but the onboarding context is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a USDC top-up intent before transferring funds: provide from_address and follow the returned transfer instructions. Alternatively, get a Stripe checkout link for your human operator to complete.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | Amount in USD to add | |
| method | Yes | Payment method | |
| api_key | Yes | Your API key | |
| tx_hash | No | Legacy optional field; a transaction hash does not replace from_address or verify payment. | |
| from_address | No | Required for USDC: the exact EVM wallet address you will send from. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure, and it does: the tool creates an intent rather than executing the transfer, returns transfer instructions, and routes Stripe through a human operator. This meaningfully informs an agent of side-effecting scope and next steps.
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 with no redundancy; the core action and the alternative are front-loaded. Every clause adds value, and the structure is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and five parameters, the description explains both return types (transfer instructions and Stripe checkout link) and the intended sequence. It could mention error cases or post-payment credit flow, but is sufficient for an agent to invoke it correctly.
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 the baseline is 3. The description adds minimal new parameter meaning beyond emphasizing from_address and implying the method-dependent outputs, but does not need to compensate for schema 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?
The description states a specific action and resource: creating a USDC top-up intent, with a clear alternative of obtaining a Stripe checkout link. This distinguishes it from sibling data-access and purchase tools, though without naming them 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?
The description gives concrete usage context: call this before transferring USDC funds and follow the returned instructions, or use it to get a Stripe checkout link for manual completion. It does not explicitly contrast with siblings like purchase_data, but the conditional method guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retained Merkle-membership check for one depin_onchain observation: stamp_day (YYYYMMDD), leaf_index (0-based row in /attestations/batch_.tsv), expected_row_sha256. verified=true ONLY when the row at that index hashes to expected_row_sha256 AND its inclusion path reaches that day's manifest merkle_root. Says nothing about Bitcoin status or upstream accuracy. Other datasets: unsupported (no retained proof).
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | ||
| stamp_day | Yes | ||
| leaf_index | Yes | ||
| expected_row_sha256 | Yes |
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 well: it gives the exact truth conditions ('verified=true ONLY when ... AND ...'), defines the leaf index and row hash, and flags what the result does not certify. It stops short of describing the full response/error shape, so it is 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?
Three dense sentences, no filler and no restating of the tool name. The main purpose and parameters are front-loaded, and each clause (format, row path, AND condition, limitations) 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 4-parameter verification tool with no annotations and no output schema, the description covers inputs, verification semantics, dataset restrictions, and interpretation caveats. The only mild gap is that it doesn't explicitly state the output container for verified (e.g., a boolean field), though it is strongly implied by 'verified=true.'
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?
Even though schema description coverage is 0%, the description defines every input's meaning: stamp_day's format, leaf_index's 0-based row location in '/attestations/batch_<day>.tsv', and expected_row_sha256's role in the hash check. It also ties dataset to the only supported value, depin_onchain.
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 opens with a concrete verb and resource: 'Retained Merkle-membership check' for 'one depin_onchain observation', which immediately identifies its role. It also distinguishes itself from broader provenance/ledger tools by stating it verifies inclusion only and 'says nothing about Bitcoin status or upstream accuracy.'
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 states the supported dataset and explicitly excludes others: 'Other datasets: unsupported (no retained proof).' The limitation 'Says nothing about Bitcoin status or upstream accuracy' also tells the agent when not to rely on this tool's result. It does not name a specific sibling alternative, but the exclusion is clear enough.
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. Dates show when Glama detected each change.
verify_footprint7 fields changedInput schema / properties / dataset / descriptionRemoved value: -"Dataset name"Input schema / properties / dataset / enumAdded value: +[
+ "depin_onchain"
+]Input schema / properties / expected_row_sha256Added value: +{
+ "pattern": "^[0-9a-f]{64}$",
+ "type": "string"
+}Input schema / properties / leaf_indexAdded value: +{
+ "minimum": 0,
+ "type": "integer"
+}Input schema / properties / record_idRemoved value: -{
- "description": "Record ID to verify",
- "type": "integer"
-}Input schema / properties / stamp_dayAdded value: +{
+ "type": "string"
+}Input schema / requiredPrevious value: -[
- "dataset",
- "record_id"
-]New value: +[
+ "dataset",
+ "stamp_day",
+ "leaf_index",
+ "expected_row_sha256"
+]topup_credits3 fields changedInput schema / allOfAdded value: +[
+ {
+ "if": {
+ "properties": {
+ "method": {
+ "const": "usdc"
+ }
+ },
+ "required": [
+ "method"
+ ]
+ },
+ "then": {
+ "required": [
+ "from_address"
+ ]
+ }
+ }
+]Input schema / properties / from_addressAdded value: +{
+ "description": "Required for USDC: the exact EVM wallet address you will send from.",
+ "pattern": "^0x[0-9a-fA-F]{40}$",
+ "type": "string"
+}Input schema / properties / tx_hash / descriptionPrevious value: -"USDC transaction hash (for crypto payments)"New value: +"Legacy optional field; a transaction hash does not replace from_address or verify payment."get_data_dictionaryget_derivation_ledgerregister_agent1 field changedInput schema / requiredPrevious value: -[
- "email",
- "agent_name"
-]New value: +[
+ "agent_name"
+]register_agent1 field changedInput schema / properties / email / descriptionPrevious value: -"Contact email (for delivery and topup notifications)"New value: +"OPTIONAL contact email (for delivery and topup notifications). Omit to register anonymously."fetch_dataset1 field changedInput schema / properties / dataset / descriptionPrevious value: -"Dataset name (e.g., depin_stats)"New value: +"Dataset name (e.g., depin_onchain)"fetch_dataset1 field changedInput schema / properties / dataset / descriptionPrevious value: -"Dataset name (e.g., master_leads)"New value: +"Dataset name (e.g., depin_stats)"get_zk_provenance3 fields changedInput schema / properties / lead_idRemoved value: -{
- "description": "Lead ID to get provenance for",
- "type": "integer"
-}Input schema / properties / record_idAdded value: +{
+ "description": "Record ID to get provenance for",
+ "type": "integer"
+}Input schema / requiredPrevious value: -[
- "lead_id"
-]New value: +[
+ "record_id"
+]purchase_data1 field changedInput schema / properties / product_key / descriptionPrevious value: -"Product key to purchase (e.g. houston_metro_leads, foreclosure_leads)"New value: +"Product key to purchase (e.g. dag_pro, mcp_unlimited)"check_balancefetch_datasetget_statsget_zk_provenancelist_datasetslist_productspurchase_dataregister_agenttopup_creditsverify_footprintClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
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The server is experiencing an outage
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