certified-mcp
certified-mcp
给你的智能体一个它无法靠嘴皮子绕过去的东西。
一个 MCP 服务器,将证书验证、等价性证明和预注册密封暴露为工具。一个修改了设计、证明或基准配置的智能体,没有任何办法检查自己的工作——所以它只会报告成功。这些工具返回的裁决是从工件本身重新推导出来的,而不是对工件的主观断言。
安装
pip install "certified-mcp @ git+https://github.com/nickharris808/certified-mcp.git@main"尚未发布到 PyPI。 名称 certified-mcp 尚未注册,所以 pip install certified-mcp 目前什么都装不了——请使用上面那一行,它安装的是完全相同的代码。当这个包发布后,这条说明会删除,直接用普通名称即可。
它的四个依赖项以 git 引用形式声明,所以那一行也会一并拉取它们。
Related MCP server: agent-gate
30 秒快速上手
在你的 MCP 客户端配置(Claude Desktop、Cursor 或任何 MCP 主机)中添加:
{
"mcpServers": {
"certified": { "command": "certified-mcp" }
}
}然后向你的智能体提出一个它原本只能靠猜的问题:
"我重构了这个加法器。证明它仍然与原始版本等价。"
prove_equivalence(inputs=["a","b"], circuit_a=[...], circuit_b=[...])
-> {"verdict": "EQUIVALENT", "receipt_verifies": true}或者,当它不等价时:
-> {"verdict": "COUNTEREXAMPLE", "counterexample": {"1": true, "2": false}}智能体会得到一个具体的失败输入,而不是"看起来没问题"。
工具
工具 | 功能 |
| 从证书自身的数字重新推导制造准入裁决;检查完整性;拒绝未证明任何内容的证书包 |
| 将 REFUTED/REJECT 裁决转化为具体清单:哪些位点是安全的、不安全的或跨界的,每个位点的裕量、所需裕量以及差距有多大。不计算任何新内容——它只是重新呈现裁决所依据的算术 |
| 在已提交的公式上重新运行 DRAT 证明(或重新模拟反例) |
| 证明两个小型组合电路等价,或返回一个产生差异的输入 |
| 检查来自任何求解器的 DRAT 反驳;指出第一个不成立的引理 |
| 在测量之前密封验收标准,且不泄露它们 |
| 检测密封后被修改的标准 |
| 根据失败图谱对验证器进行评分 |
| 解释缺陷类别:为什么伪造看起来有效,以及什么能抓住它 |
为什么智能体特别受益
这直接针对三种失败模式:
自信的错误。 重构逻辑的智能体会说它保持了行为不变。 当它没有保持时,
prove_equivalence会返回一个反例输入。移动球门柱。 针对基准调优的智能体会悄悄放宽阈值。 在运行前使用
seal_criteria可以让这一点可被检测——包括被智能体自己检测到。信任别人递给它的证明。
check_drat接受来自任何求解器的证明并重新检查 每一个引理,所以伪造的证明会被抓住而不是被引用。
这里的一切都是本地的、只读的
无网络。不上传任何内容。无遥测。每个工具要么读取你指定的文件,要么对你传入的参数进行计算。
这些工具中没有一个能生成制造证书——只能检查证书。这种不对称是 故意的,并且由测试强制执行。检查是廉价的,应该无处不在;生成一份值得检查的 证书需要认证引擎,那是一个独立的闭源产品。
实现
仅使用标准库,MCP stdio 协议,约 300 行。你可以在决定运行它之前阅读整个 服务器——对于要接入一个具有文件系统访问权限的智能体的东西,你应该这样做。
许可证
Apache-2.0。
诚实的范围——这些工具证明了什么,以及它们不能证明什么
问题 | 答案 |
智能体可以用这些工具检查证书、证明或密封吗? | 可以,全部在本地且只读。 |
| 不是。 它意味着工具因为缺少信任锚而弃权。智能体绝不能将其报告为通过或失败。 |
这里的任何工具能生成证书吗? | 不能——由测试强制执行。这些是检查器。 |
这里有任何东西验证物理吗? | 永远不会。 |
工具包的其他部分
记录的裁决是需要检查的主张,绝不是可以信任的输入。 九个仓库都建立在这个原则之上。
完整的故事以及被回答的质疑,都在 certified-oss ——如果这是你打开的第一个,请从那里开始。
重新推导制造证书的裁决。仅标准库。 | |
证明两个电路等价,并附带任何人都能重新检查的收据。 | |
在测量之前密封验收标准。 | |
28 个带标签的伪造品和一个任何退化验证器都无法获胜的指标。 | |
以上所有内容,作为你的 AI 智能体可以调用的工具。 | |
浏览器中的验证器。不上传任何内容。 |
现在试试,无需安装: 🔏 验证器 Space · 浏览伪造品: 📊 图谱数据集
免费版止步之处
这里的一切都是检查。没有一个是生成具有物理意义的证书——那需要 对真实过程模型进行可靠的包络计算,这是一个独立的商业产品。如果你 需要证书而不是检查证书的方法,那才是需要进行的对话。
文档
PERFORMANCE.md — 实测数据,包括未优化的部分
CONTRIBUTING.md — 贡献指南
全组合集:教程 · 概念 · 常见问题 · 架构 · API 参考
许可证、引用、贡献
Apache-2.0——见 LICENSE。如果你使用这个项目,请引用它: CITATION.cff。
最有价值的贡献是一个本项目未能识破的伪造品—— 见 CONTRIBUTING.md 和 全组合集指南。
记录的裁决是需要检查的主张,绝不是可以信任的输入。
certified-mcp 是建立在这个原则之上的九个仓库之一。完整的故事以及被回答的质疑,
都在 certified-oss ——如果这是你
打开的第一个,请从那里开始。
Available Tools
9 toolscheck_dratA
Check a DRAT refutation against a CNF in DIMACS form. Accepts proofs from any solver. Returns whether every lemma is RUP and, on failure, the index and content of the first lemma that does not follow.
| Name | Required | Description | Default |
|---|---|---|---|
| cnf_path | Yes | ||
| drat_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses what the tool returns, including failure details. It implies a read-only check ('returns') and mentions flexibility in accepting proofs from any solver. It does not explicitly state side effects or limitations, but for a check operation this is sufficient.
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, front-loaded with the main action, and contains no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
It covers the core behavior (check, input formats, output on success/failure) concisely. It lacks details on error handling or return format, but given the tool's simplicity and no output schema, it's largely 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 coverage is 0%, so the description must explain the parameters. It implies two file paths (CNF and DRAT) through the description but does not explicitly name the parameters. The meaning of 'RUP' and 'lemma' may be unclear to non-experts. Sufficient but not fully explicit.
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 the operation: checking a DRAT refutation against a CNF in DIMACS form. It distinguishes itself from sibling tools (e.g., explain_certificate, verify_certificate) by focusing on RUP lemma verification with specific failure details.
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 provides context on when to use (for any DRAT proof from any solver) and what it verifies (RUP lemmas). It does not explicitly mention alternatives or when not to use it, but the scope is evident from the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_sealA
Check that an acceptance specification still matches its seal. Detects criteria that were changed after sealing. Returns matched=false with the two digests if they diverge.
| Name | Required | Description | Default |
|---|---|---|---|
| seal | No | ||
| spec | Yes | ||
| seal_path | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It partially succeeds by stating the return behavior: 'Returns matched=false with the two digests if they diverge.' This gives insight into the tool's output and failure mode. It does not state whether the tool has side effects, but 'check' strongly implies a read-only operation, and the core verification behavior is transparent enough.
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 concise and front-loaded, with three short sentences that each add value: what the tool does, what it detects, and what it returns. There is no redundant phrasing or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and three parameters with nested objects, the description is minimally viable: it clearly states the purpose and the divergent return case. However, it omits parameter semantics, the matched=true return case, and does not provide enough context for an agent to confidently decide between passing a seal object vs a seal_path. It is acceptable but has clear gaps.
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 schema's lack of explanations. It mentions 'acceptance specification' and 'seal' generally, but does not clarify the roles of the three parameters (seal, spec, seal_path), which are required vs optional, or how seal_path relates to the seal object. The parameter semantics are severely under-specified.
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+resource construction ('Check that an acceptance specification still matches its seal') and clearly distinguishes itself from siblings like seal_criteria and verify_certificate by focusing on detecting post-sealing changes. It also names the concrete detection scope ('criteria that were changed after sealing'), so there is no ambiguity about the tool's function.
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 usage context is implied through 'Detects criteria that were changed after sealing,' suggesting this is for verifying that a spec has not been modified since it was sealed. However, there is no explicit 'use this when...' statement, no mention of when not to use it, and no alternatives are named among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_certificateA
Explain WHY a certificate reached its verdict, locus by locus: which loci are safe, unsafe or straddling, the margin each had, the margin it needed, and how far short it fell. Use this after verify_certificate returns REFUTED or a certificate records a REJECT — it turns an opaque refusal into a specific, actionable list. Computes nothing new; it re-presents the arithmetic the verdict already rests on.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | how many binding loci to list | |
| bundle_dir | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it states 'Computes nothing new; it re-presents the arithmetic the verdict already rests on,' disclosing the read-only, non-mutating nature. It could add more about output formatting or errors, but the key behavioral trait is covered.
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, front-loaded with the core verb ('Explain WHY a certificate reached its verdict'). Every sentence adds value: purpose, usage trigger, and a transparency note. 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 only two parameters and no output schema, the description is largely complete: it covers purpose, usage timing, and behavioral transparency. The only gap is the unmentioned `bundle_dir` parameter meaning, which is inferable from context but not explicit.
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 describes `limit` but not `bundle_dir`. The description indirectly implies bundle_dir is the certificate directory but doesn't explicitly define it. This partial compensation for the 50% schema coverage is adequate but not thorough.
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 explains why a certificate reached its verdict, listing specific outputs (safe/unsafe/straddling loci, margins, shortfalls). It also distinguishes from siblings by referencing verify_certificate and REJECT/REFUTED verdicts, making it unique among tools like explain_defect.
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 states when to use it: 'Use this after verify_certificate returns REFUTED or a certificate records a REJECT.' It also explains the benefit ('turns an opaque refusal into a specific, actionable list'), leaving no ambiguity about its appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_defectA
Explain a certificate defect class from the atlas taxonomy: why the forgery looks valid, and which check catches it. Call with no key to list every defect.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No |
TDQS
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 clarifies this is an explanation (non-mutating), details the type of information returned, and documents the effect of omitting the key. It does not describe error handling or return structure, but the core behavior is transparent.
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 well-structured sentences: the first states the core purpose and output, the second provides a distinct call mode. No wasted words, and key information is front-loaded.
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 tool with one optional parameter and no output schema, this description is sufficient. It states the purpose, the nature of its output, and a special usage mode. It could be enhanced by describing the response format, but the current description covers the essential 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 coverage is 0%, so the description must illuminate the 'key' parameter. It indicates that providing a key selects a specific defect class and that omitting it lists all defects, which gives meaningful usage context. However, it does not specify the expected format or domain of the key.
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 ('explain') with a defined resource ('certificate defect class from the atlas taxonomy') and explicitly describes what the explanation covers ('why the forgery looks valid, and which check catches it'). This clearly distinguishes it from sibling tools like explain_certificate.
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: this tool is for defect classes in the atlas taxonomy, and it documents the special behavior of calling with no key to list all defects. It does not explicitly mention alternatives or when not to use, but the purpose statement gives enough context to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
prove_equivalenceA
Prove two small combinational circuits equivalent, or return a counterexample input. Circuits are given as gate lists over named signals. Returns a receipt any third party can re-check. Small instances only — this is a demonstration prover, not a production one.
| Name | Required | Description | Default |
|---|---|---|---|
| inputs | Yes | Primary input names, e.g. ["a","b"]. | |
| out_path | No | Optional path to write the receipt. | |
| circuit_a | Yes | Gates: {op: AND|OR|NOT|XOR, out: name, args: [names]}. | |
| circuit_b | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the counterexample behavior, receipt output, and size limitations, which are meaningful behavioral traits beyond just 'prove'. It does not mention side effects, but none are apparent for a demonstration prover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each with distinct value: action, input/output format, and limitations. No wasted words, and the core purpose is front-loaded.
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-param tool with no output schema, the description covers key behavioral aspects: equivalence proof, counterexample, receipt, and scope limitation. It does not specify receipt contents, but sibling tools like verify_receipt handle that aspect, so this is acceptable.
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?
Input schema covers 75% of parameters with descriptions. The description adds context about circuits being gate lists over named signals, which applies to both circuit_a and circuit_b, but does not elaborate on out_path or parameter-specific details beyond what the schema already provides.
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 ('prove') and resource ('two small combinational circuits') and clearly distinguishes from sibling certificate-verification tools by stating the core equivalence-checking behavior. It also mentions the counterexample output, which further clarifies the scope.
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 states size limitation ('Small instances only') and that it is a demonstration prover, not for production. This gives clear guidance on when to use it, though it does not explicitly name alternative tools for larger instances.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
score_verifierA
Score a verifier command against the certificate failure atlas. Returns detection (forgeries rejected), precision (valid artifacts accepted), and atlas_score = the minimum of the two, plus exactly which forgeries got through.
| Name | Required | Description | Default |
|---|---|---|---|
| command | Yes | argv with {path} as the artifact placeholder. | |
| atlas_dir | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behaviors. It does specify the output (detection, precision, atlas_score, and forgeries that got through), which is useful. But it does not disclose side effects, execution behavior, permissions, or prerequisites, leaving the transparency incomplete for a tool that likely executes a command.
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 consists of two efficient sentences. The first states the action and target, the second lists the return values. No fluff or redundancy; it is well-structured and front-loaded.
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 tool with 2 parameters and no output schema, the description is nearly complete: it conveys the purpose, the input (implicitly via 'verifier command' and 'atlas'), and the exact output metrics. It only lacks explicit parameter enumeration and usage scenarios, which are minor gaps.
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 50% of parameters: 'command' is described as argv with {path} placeholder, while 'atlas_dir' has no description. The tool description contextually links atlas_dir to the certificate failure atlas but does not explicitly explain its format or role. It adds some context but does not fully compensate for the uncovered 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 uses the specific verb 'Score' against a concrete resource (the certificate failure atlas), and explicitly lists the calculated metrics (detection, precision, atlas_score). This clearly distinguishes the tool from sibling verification/explanation tools by indicating an evaluation/benchmarking 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?
Usage is implied by 'Score a verifier command against the certificate failure atlas', which suggests using this when evaluating a verifier. However, there is no explicit statement of when to use this tool versus siblings like verify_certificate or check_drat, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
seal_criteriaA
Seal an acceptance specification BEFORE measuring, so it cannot be adjusted afterward. Returns a digest that commits to the criteria without revealing them. Call this before running an experiment, not after.
| Name | Required | Description | Default |
|---|---|---|---|
| note | No | ||
| spec | Yes | ||
| out_path | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns a digest that commits to criteria without revealing them, and it enforces immutability ('cannot be adjusted afterward'). While it does not cover failure modes or required permissions, it provides substantial behavioral context for a sealing operation.
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, efficient and front-loaded. The first sentence states the core action and purpose, the second sentence explains the return value and timing. Every sentence earns its place with 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 explains the core concept and timing well, but is incomplete in crucial areas. It does not explain the return digest format or the parameters, especially the nested spec object and optional note/out_path. For a tool with no annotations, output schema, or parameter descriptions, this leaves the agent with insufficient information 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?
The description provides zero information about the parameters. The schema lists three parameters (spec, note, out_path) with no descriptions, and the description mentions none of them. With 0% schema description coverage, the description fails to compensate, leaving the agent without guidance on what to pass for each 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 states the tool's action ('Seal an acceptance specification') and its specific purpose (to prevent adjustment after measuring). It also distinguishes itself from sibling tools by emphasizing the pre-measurement timing and the commitment aspect, which is unique among the listed siblings focused on verification and explanation.
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 timing guidance: 'Call this before running an experiment, not after.' This clearly indicates when to use the tool and when not to. However, it does not explicitly mention alternative tools or provide comparative use cases, which would elevate it to a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_certificateA
Verify a manufacturing certificate bundle. Re-derives the admission verdict from the certificate's own numbers rather than reading it, and checks integrity. Returns a verdict: VERIFIED, REFUTED, VACUOUS, or UNVERIFIED. IMPORTANT: without an expected_sha256 (a fingerprint obtained OUT OF BAND, not from the bundle itself) the verdict is UNVERIFIED — the tool abstains, because internal consistency alone cannot rule out a forgery whose inputs and verdict were edited together. UNVERIFIED means 'cannot tell', NOT 'the certificate is bad'. Do not report it as either pass or fail.
| Name | Required | Description | Default |
|---|---|---|---|
| bundle_dir | Yes | Path to the bundle directory. | |
| allow_empty | No | ||
| expected_sha256 | No | The out-of-band fingerprint — the trust anchor. Without it the tool abstains. | |
| accept_without_anchor | No | Accept the weaker internal-consistency check on purpose. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so thoroughly. It discloses the re-derivation approach, integrity checking, the four possible verdicts, the abstention logic when expected_sha256 is missing, and the crucial semantic distinction that UNVERIFIED means 'cannot tell' rather than 'bad'. This goes well beyond a minimal behavioral summary.
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 four sentences long, each earning its place. It front-loads the purpose, then efficiently covers the method, return values, and the critical caveat about UNVERIFIED. There is no fluff or 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?
For a tool with no output schema, the description lists all verdict values, explains the abstention case, and clarifies how to interpret UNVERIFIED. It is quite complete, though a minor gap exists: the description does not reconcile the existence of accept_without_anchor with the absolute statement that without expected_sha256 the verdict is UNVERIFIED.
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 75% (three of four parameters have descriptions), and the description adds significant meaning to expected_sha256 by explaining that it is an out-of-band trust anchor and why it is necessary to prevent forgeries. However, allow_empty has no schema description and is not mentioned in the description, and the interaction between accept_without_anchor and the stated 'without expected_sha256 the verdict is UNVERIFIED' rule is left ambiguous.
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 'Verify a manufacturing certificate bundle,' which is a specific verb and resource. It goes further to explain the verification method—'re-derives the admission verdict from the certificate's own numbers rather than reading it, and checks integrity'—which clearly distinguishes this from simply reading the stored verdict and from sibling tools like verify_receipt or explain_certificate.
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 usage context: it stresses that without an out-of-band expected_sha256 the tool abstains with UNVERIFIED, and it explicitly warns not to report UNVERIFIED as pass or fail. This is strong guidance for when the tool's output is trustworthy, though it does not name alternative tools for specific scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_receiptA
Verify a logic-equivalence receipt. Re-runs the DRAT proof check (or re-simulates the counterexample) over the committed formula, and recomputes the hash chain. The verdict is re-derived, never read from the receipt.
| Name | Required | Description | Default |
|---|---|---|---|
| receipt_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the DRAT check is re-run, the counterexample is re-simulated, and the hash chain is recomputed, emphasizing that the verdict is re-derived rather than read from the receipt. This builds trust without needing 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?
Three concise sentences front-load the purpose, then add process details, and finally emphasize the trust model. No filler or 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 tool has one parameter and no output schema, but the description does not state what the tool returns (e.g., a verdict) or how the result is presented. It also lacks guidance on how this differs from sibling tools, leaving some gaps given the complex domain of proof verification.
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 0% and the only parameter, receipt_path, is not explained in the description. The description refers to 'the receipt' but never clarifies what the path should point to or any constraints, leaving the parameter semantics to the name alone.
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 'Verify a logic-equivalence receipt,' which is a specific verb and resource. It clearly distinguishes from siblings like verify_certificate by focusing on receipts, and adds details about re-running DRAT proof checks and recomputing hash chains.
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 the tool (when you have a receipt to verify) but does not explicitly contrast it with alternatives like check_drat or verify_certificate. No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
9 tool updates
v1.0.0- First observed
check_drat - First observed
check_seal - First observed
explain_certificate - First observed
explain_defect - First observed
prove_equivalence - First observed
score_verifier - First observed
seal_criteria - First observed
verify_certificate - First observed
verify_receipt
TDQS
Scored across 9 tools
Most tools have distinct purposes, but the verify_* family (verify_certificate, verify_receipt) and check_drat all involve checking proofs, which could cause confusion. However, the descriptions clearly differentiate artifact types and workflows.
Tool names consistently follow a verb_noun pattern (e.g., verify_certificate, check_drat, explain_defect). No mixed conventions or vague verbs; every name conveys its action and target.
With 9 tools, the server is well-scoped for a certification/verification domain. Each tool serves a clear role, and the count is within the ideal 3-15 range.
The toolset covers the core verification lifecycle: proving, checking, verifying, explaining, and sealing. Minor gaps exist (e.g., no tool to create certificates or manage the atlas), but these are likely external to this server's purpose.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Tamper-evident proof creation and verification for AI agents via MCP, A2A, and REST.
Trust checks for MCP servers: trust scores, tool-drift detection, signed diligence receipts. Free.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
MCP server for building and testing AI agents with multi-model experimentation and insights.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceAn MCP server that provides fact-checking capabilities and truth anchoring for AI agents using verified data sources.MIT
- AlicenseBqualityBmaintenanceAn MCP server that enforces fail-closed deterministic checks, independent refute-first review, and tamper-evident hash-chained receipts for AI agent outputs before claiming completion.43MIT
- AlicenseAqualityAmaintenanceMCP server for checking supply-chain trust before connecting to AI agents, frameworks, or MCP servers.8731MIT
- AlicenseAqualityDmaintenanceMCP server that enables AI agents to verify each other's trust scores, register, submit reviews, and find trusted agents before transacting.424MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/nickharris808/certified-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server