Evidence about ERC-8004 agents on BNB Smart Chain, gathered by calling them. No key.
List the agents one wallet registered on BSC and call each of their declared endpoints now. Nothing on the chain tells an owner their endpoint went dark — the registry keeps listing it — so this is the answer to "is my agent still answering?": what answered, how each failure looks, and every probe Kawal kept. At most 12 agents are dialled, so a large owner sees the first page.
| Name | Required | Description | Default |
|---|---|---|---|
| owner | Yes | The wallet that minted the registrations: 0x followed by forty hex characters. `address` is accepted as an alias. | |
| chainId | No | Chain id. One of 56, 97. Defaults to 56. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a read-only, idempotent operation. The description adds that it makes live network calls to each agent's endpoint, discloses a limit of 12 agents, and hints at the output containing probe results and failures, enriching behavior beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient, front-loading the core action, then explaining rationale, limit, and output in minimal sentences. No wasted words; every sentence contributes to understanding.
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 purpose, live-call behavior, the 12-agent cap, and what output to expect (answers, failures, recorded probes). While there's no output schema, it provides enough for an agent to know what it will get, without detailing exact structure.
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 fully documents both parameters (owner format and chainId defaults), providing 100% coverage. The description adds no extra parameter details, so a baseline score of 3 is appropriate given high 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 tool lists agents for a wallet and calls their endpoints immediately, defining the purpose as checking if agents are answering. It distinguishes from siblings by focusing on live probing rather than historical or single-agent checks, with a specific verb and resource.
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 explicitly frames the tool as the answer to 'is my agent still answering?', signaling when to use it. It doesn't name alternatives or exclusions, but the use case is clear enough to guide selection over historical uptime tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Send the opening request of the x402 protocol — no payment header — and report whether the server actually demands payment. x402_supported on the registry is a flag a registration sets about itself; this is the answer to asking. Kawal never settles a payment, so the price is quoted in the server's own words and nothing moves.
| Name | Required | Description | Default |
|---|---|---|---|
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| tokenId | Yes | ERC-8004 token id, decimal digits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive behavior, and the description adds meaningful behavioral detail: it sends a request with 'no payment header', never settles a payment, quotes the server's own words, and 'nothing moves.' This clearly discloses what happens and what does not happen, with no contradiction against the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the primary action, then adds a useful distinction from the registry flag and a safety clarification about payments. Each sentence contributes meaningful guidance without redundancy or 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 read-only probe with only two parameters and full schema coverage, the description is nearly complete. It explains the protocol behavior, the output concept ('report whether the server actually demands payment'), and the safety stance. It could be slightly more explicit about the exact response shape since there is no output schema, but the current wording is sufficient for 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?
Schema description coverage is 100%, so the input schema fully documents `chainId` and `tokenId`. The description does not add parameter-level detail, but given full schema coverage, the baseline of 3 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 a clear specific action: 'Send the opening request of the x402 protocol — no payment header — and report whether the server actually demands payment.' It identifies the resource (the agent server), the operation (probe), and the outcome (boolean-style report). It also distinguishes itself from merely reading the self-reported registry flag.
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 clear context for when to use this tool: when you need the empirical answer to whether an agent really charges, rather than trusting the registration's self-declared `x402_supported` flag. It does not explicitly name a sibling alternative or list when-not-to-use conditions, but the contrast with the registry flag is enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Put two or three agents side by side and answer the same questions of each — the ones a buyer weighs before handing over a spend cap: can it be hired, does it answer right now, has it kept answering, what does it offer, what does it say it costs, does its domain verify, what is its track record, what is flagged against it, and when it was registered. Each agent is dialled for this call. No combined score: the disagreements are the point.
| Name | Required | Description | Default |
|---|---|---|---|
| agents | Yes | One to 3 agents to compare. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent/destructive annotations, the description discloses that each agent is queried individually, the same question set is applied to every agent, and the output intentionally highlights disagreements. It does not detail output format or errors, but annotations already cover the safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the action and resource, then lists the concrete questions each agent is asked. It is a little wordy, but each sentence contributes useful decision context, and the closing note about disagreements earning 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 read-only comparison tool, the description covers purpose, scope, question criteria, and core output behavior even without an output schema. The main minor gap is the mismatch between the description's 'two or three' and the schema's minimum of one agent, plus the unstated exact return shape.
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 description adds little parameter-specific value. It reinforces the two-to-three agent intent, but it does not clarify behavior when the schema permits a single agent, which is a slight 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 clearly states that the tool places two or three agents side by side and answers the same buyer-oriented questions for each. It distinguishes compare_agents from sibling tools by emphasizing side-by-side comparison and explicitly noting there is no combined score.
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 clear context: use this tool when evaluating agents before awarding a spend cap, and want to see disagreements rather than an aggregate. It does not explicitly name alternatives or say when not to use the tool, but the intended use case is well implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Everything Kawal holds about one agent in a single answer: the live handshake, the full probe history, how the endpoint fails when it fails, whether it really charges, and who wrote its feedback. This one costs money. Kawal measured that 75 of 200 BSC registrations declare x402 support and that none of the reachable ones ever asks to be paid; this is the counter-example. Calling it without payment returns the terms rather than the report. Pay the terms with a plain BNB transfer and call again with txHash; the receipt is read on-chain and spent once.
| Name | Required | Description | Default |
|---|---|---|---|
| txHash | No | Hash of the BNB transfer that paid the terms: 0x followed by 64 hex characters. Omit to receive the terms. | |
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| tokenId | Yes | ERC-8004 token id, decimal digits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses critical behavioral traits beyond the annotations: the call costs money, unpaid calls return terms instead of the report, the txHash receipt is consumed exactly once, and payment is verified on-chain. This is exactly the kind of non-obvious behavior an agent must know before 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?
The description front-loads the report contents and keeps the payment flow clear. The BSC measurement sentence adds helpful context for why the tool costs money, but it is slightly non-essential for invocation and lengthens the 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?
For a paid, non-idempotent, one-agent report with no output schema, the description fully covers the invocation lifecycle, payment requirement, one-time txHash semantics, and expected contents. There is no critical missing information for an agent to call 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 coverage is 100% and the schema already documents each parameter. The description adds valuable context by explaining that omitting txHash returns terms, and that the same txHash is spent once, which is essential for correct use of the optional parameter.
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 identifies what the tool returns: a single comprehensive report about one agent, with enumerated contents like live handshake, probe history, failure mode, charging status, and feedback authorship. It doesn't use an explicit verb like 'get' or name sibling tools, but the 'everything... in a single answer' phrasing and paid marker distinguish it from lighter lookups.
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 a clear two-step usage flow: call without txHash to receive terms, pay with a BNB transfer, then call again with txHash, with the receipt read on-chain and spent once. It does not explicitly state when to prefer this over siblings, but it makes the required sequence unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the BSC roster by describing the problem rather than naming a product, and get back agents ranked with Kawal's evidence attached. Duplicate registrations are collapsed: roughly two thirds of the newest registrations are copies of a template, and returning all of them would be returning the same agent many times. With trending, the list is instead what 8004scan's visitors looked at most over that window — attention, not evidence — each entry carrying the tier Kawal's own record gives it, which is where the two disagree.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many to return, at most 20. | |
| query | No | What you need done, in plain words. Optional when `trending` is given; then it filters the trending list by name or description. | |
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| trending | No | Return 8004scan's trending agents for this window instead of a search, in the registry's order. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds meaningful behavioral context: duplicate registrations are collapsed, trending returns attention-based results rather than evidence-based ones, and each entry carries Kawal's tier. This materially helps an agent understand what the tool will actually return.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, and every subsequent sentence adds relevant behavioral detail about duplicates and trending. It is dense but not bloated, with 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?
Given the 100% parameter schema coverage, rich annotations, and no output schema, the description provides enough context for an agent to call this tool correctly: it explains search behavior, trending behavior, duplicate handling, and result ranking. No critical selection or invocation information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already fully documents each parameter. The description adds semantic value by explaining how `query` behaves when `trending` is given, how `trending` changes the result semantics from evidence to attention, and why duplicate collapsing matters.
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 searches the BSC roster by problem description and returns ranked agents with Kawal's evidence attached, which distinguishes it from product-name lookups. It does not explicitly name alternative sibling tools, but the purpose is specific and well-scoped.
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 clear context on when to use the search mode versus the trending mode, and explains that query is optional when trending is used. It does not explicitly enumerate when-not-to-use cases or alternatives among sibling tools, but the guidance is sufficient for selecting the right mode.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turn a capital amount and a duration into the four scoped sessions Kawal would grant on Altana — one per seat, each with its own contract allowlist, spend cap and expiry — without granting anything. Pure arithmetic over the venue table: the same plan the mandate form shows, and the same refusals, since a plan that would widen authority (an unproven venue, an empty allowlist, caps that overcommit) is refused rather than built.
| Name | Required | Description | Default |
|---|---|---|---|
| days | Yes | How long the mandate runs, in whole days. 1 to 365. | |
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| capitalUsdt | Yes | Capital to entrust, in USDT. Positive, at most 1000000000000. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly, idempotent, non-destructive), the description adds meaningful behavior: it is pure arithmetic over the venue table, produces the same refusals as the mandate form, and refuses plans that would widen authority—unproven venues, empty allowlists, and overcommitting caps. This gives an agent concrete expectations about determinism and failure cases.
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, front-loaded with what the tool does and followed by the key safety distinction: it grants nothing. Every clause earns its place, and the refusal conditions are compactly listed without bloat.
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 rich annotations, complete schema coverage, and the description's explanation of both output shape (four seat sessions with allowlist, spend cap, expiry) and refusal behavior, the definition is fully sufficient for an agent to select and invoke the tool correctly—even without an output schema.
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 fully documents all three parameters with descriptions, types, and bounds. The tool description mentions capital amount and duration but adds no new parameter-level meaning; the baseline of 3 is appropriate since the schema carries the semantics.
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 names a specific operation: turning a capital amount and duration into four bounded sessions without granting anything. It also clarifies the scope of each session (contract allowlist, spend cap, expiry), which distinguishes this planning/preview tool from any actual granting action.
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 makes clear this is the tool to use when you want the exact plan the mandate form would show, including refusals, without making any grant. It does not name alternative sibling tools or provide explicit when-not-to-use guidance, but the context is clear enough for an agent to route correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report who wrote an agent's ERC-8004 feedback, not how much of it there is. A sample of 1,200 BSC records came from 53 addresses, one of which wrote 265 of the oldest 600, so a count of records is a count of writes rather than of opinions. Returns how many carry a mark, how many distinct addresses wrote them, and what share came from the busiest one.
| Name | Required | Description | Default |
|---|---|---|---|
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| tokenId | Yes | ERC-8004 token id, decimal digits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by explaining analytical behavior: it returns a count of marked records, distinct addresses, and the share from the busiest writer. It also gives a concrete sample statistic to illustrate why raw record counts can mislead about opinions. No contradiction with the readOnly/idempotent/destructive annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loads the core purpose before the clarifying caveat and return summary. The sample statistic is slightly detailed but earns its place by reinforcing why this tool measures authorship rather than opinion volume. 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?
For a read-only analysis tool with two well-documented parameters and no output schema, the description adequately explains the return semantics: marked count, distinct addresses, and busiest-writer share. It could specify behavior for invalid token IDs or unsupported chains, but the annotations and schema cover the essential invocation context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with chainId and tokenId already documented in the schema. The description provides useful domain context about ERC-8004 and BSC records, but it does not add meaning beyond what the schema already communicates for either parameter. Baseline 3 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 uses a specific verb and resource: "Report who wrote an agent's ERC-8004 feedback." It also distinguishes itself from a mere quantity tool by explicitly saying it is "not how much of it there is," making its purpose 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?
The description clearly frames when this tool is appropriate: when you care about authorship of feedback rather than aggregate volume. It does not explicitly name sibling alternatives or state when not to use it, but the contrast with "not how much" provides usable selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the history Kawal keeps of one agent's endpoint without dialling it: how many times it was called, how many times it answered in the protocol it declared, since when, and the median and worst latency of the answering calls. This is the record the registry does not have. Null when Kawal has never called this endpoint; verify_agent makes the first call.
| Name | Required | Description | Default |
|---|---|---|---|
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| tokenId | Yes | ERC-8004 token id, decimal digits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the bar for additional disclosure is lower. The description adds meaningful behavioral detail beyond annotations: it does not dial the endpoint, it returns null when Kawal has never called it, and it describes the protocol-answer filtering and latency metrics. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two dense sentences with zero filler. The main operation and return content are front-loaded, and the second sentence efficiently captures the negative case and the relationship to verify_agent. Every sentence 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?
There is no output schema, so the description correctly carries the burden of explaining return values: call counts, protocol-valid responses, start time, median and worst latency, and null when absent. It also clarifies the 'without dialling' side effect and names the action that creates the first record. The tool is fully understandable for a correct call without looking elsewhere.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both chainId and tokenId already documented concretely ('Chain id. One of 56, 97. Defaults to 56.' and 'ERC-8004 token id, decimal digits.'). The description does not add parameter-level detail, but it does not need to; the baseline of 3 applies because the schema carries the full burden.
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+resource: 'Read the history Kawal keeps of one agent's endpoint' and immediately enumerates the exact metrics returned (call count, protocol-valid answers, first-call time, median and worst latency). It also distinguishes itself from the registry and from verify_agent, making sibling differentiation clear.
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 clearly frames when to use this tool: when you need Kawal's observed probe history rather than the registry record. It also calls out the null case when Kawal has never probed the endpoint and names verify_agent as the action that makes the first call, effectively pointing to the relevant alternative. However, it stops short of an explicit 'use A instead of B' rule for all siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Call an agent's declared endpoint right now and report what answered. This is a live handshake made by Kawal, not a reading of the registry: an agent whose registration declares MCP but whose endpoint is gone is reported as not answering. Returns the hireability tier and the evidence behind it, including how many times Kawal has reached this endpoint before.
| Name | Required | Description | Default |
|---|---|---|---|
| chainId | No | Chain id. One of 56, 97. Defaults to 56. | |
| tokenId | Yes | ERC-8004 token id, decimal digits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotent, and non-destructive. The description adds valuable context beyond that: it is a live network call, can report a non-answering endpoint, and returns evidence including a reach counter. It also clarifies that it is not a registry read, which changes expectations about freshness. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise, information-dense sentences. The action is front-loaded, and the critical distinction (live vs registry) appears early. 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 read-only verification tool with full parameter documentation and annotations covering safety, the description fully explains what is returned (hireability tier, evidence, reach count) and the behavioral nuance (live handshake). Nothing an agent needs to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for both chainId and tokenId with clear descriptions. The tool description does not add any extra parameter-specific semantics beyond what the schema already provides, so a baseline 3 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 a specific verb-resource pair: live-call an agent's endpoint. It explicitly distinguishes itself from a registry read, which sets it apart from sibling tools like find_agents or uptime_history. The purpose 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?
The description implies when to use it (when you need live verification rather than registry data) by contrasting it with registry reading, but it does not name explicit alternatives or exclude conditions. It lacks a direct 'use this instead of X' but gives enough contextual signal.
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.
agents_by_ownercheck_paymentcompare_agentsdeep_reportfind_agentsplan_mandateread_reputationuptime_historyverify_agentClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Which ERC-8004 agents on BNB Chain actually answer, and what any BSC trade really costs
378k+ ERC-8004 agents and their security verifications. Two tools free, the rest USDC over x402.
On-chain ERC-8004 agent registry. Search, register, and check reputation across 16 chains.
Evidence observatory for agentic commerce: x402 preflight, receipt checks, settlement attestations.
Add one secure layer between your agents and this server.
Most tools have distinct angles—live single check, history, paid deep report, comparison, owner sweep—so misselection is unlikely in practice. However, verify_agent, uptime_history, and deep_report all probe one agent and could be confused without reading descriptions carefully.
Six of nine tools follow the verb_noun pattern (check_payment, verify_agent, find_agents), and all names are readable snake_case. The exceptions—agents_by_owner, deep_report, and uptime_history—are noticeable but not chaotic.
Nine tools is well-scoped for a monitoring and verification service. Each tool covers a distinct user need with no obvious redundancy.
The set covers search, live verification, uptime history, reputation source analysis, payment checks, comparison, owner listing, and mandate planning. Minor gaps like pagination on agents_by_owner and find_agents prevent a higher score.
